diff --git a/.gitattributes b/.gitattributes index ed50ee73774f722d6df6cf6e31f54181d647de35..222a273c53377595987c25f502d8306bdf31ac7d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -36,3 +36,35 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text checkpoints/sppo-fruitbot_easy_0_0_2021/checkpoints/25000000 filter=lfs diff=lfs merge=lfs -text checkpoints/sppo-jumper_easy_0_0_2021/checkpoints/25000000 filter=lfs diff=lfs merge=lfs -text checkpoints/sppo-ninja_easy_0_0_2021/checkpoints/25000000 filter=lfs diff=lfs merge=lfs -text +baselines/gail/result/halfcheetah-training.png filter=lfs diff=lfs merge=lfs -text +baselines/gail/result/hopper-training.png filter=lfs diff=lfs merge=lfs -text +baselines/gail/result/humanoid-training.png filter=lfs diff=lfs merge=lfs -text +baselines/gail/result/humanoidstandup-training.png filter=lfs diff=lfs merge=lfs -text +baselines/gail/result/walker2d-training.png filter=lfs diff=lfs merge=lfs -text +train_procgen/checkpoints/sppo-fruitbot_easy_0_0_2021/checkpoints/25000000 filter=lfs diff=lfs merge=lfs -text +train_procgen/checkpoints/sppo-jumper_easy_0_0_2021/checkpoints/25000000 filter=lfs diff=lfs merge=lfs -text +train_procgen/checkpoints/sppo-ninja_easy_0_0_2021/checkpoints/25000000 filter=lfs diff=lfs merge=lfs -text +videos/fruitbot_skills/cluster_0.gif filter=lfs diff=lfs merge=lfs -text +videos/fruitbot_skills/cluster_1.gif filter=lfs diff=lfs merge=lfs -text +videos/fruitbot_skills/cluster_2.gif filter=lfs diff=lfs merge=lfs -text +videos/fruitbot_skills/cluster_3.gif filter=lfs diff=lfs merge=lfs -text +videos/fruitbot_skills/cluster_4.gif filter=lfs diff=lfs merge=lfs -text +videos/fruitbot_skills/cluster_5.gif filter=lfs diff=lfs merge=lfs -text +videos/fruitbot_skills/cluster_6.gif filter=lfs diff=lfs merge=lfs -text +videos/fruitbot_skills/cluster_7.gif filter=lfs diff=lfs merge=lfs -text +videos/jumper_skills/cluster_0.gif filter=lfs diff=lfs merge=lfs -text +videos/jumper_skills/cluster_1.gif filter=lfs diff=lfs merge=lfs -text +videos/jumper_skills/cluster_2.gif filter=lfs diff=lfs merge=lfs -text +videos/jumper_skills/cluster_3.gif filter=lfs diff=lfs merge=lfs -text +videos/jumper_skills/cluster_4.gif filter=lfs diff=lfs merge=lfs -text +videos/jumper_skills/cluster_5.gif filter=lfs diff=lfs merge=lfs -text +videos/jumper_skills/cluster_6.gif filter=lfs diff=lfs merge=lfs -text +videos/jumper_skills/cluster_7.gif filter=lfs diff=lfs merge=lfs -text +videos/ninja_skills/cluster_0.gif filter=lfs diff=lfs merge=lfs -text +videos/ninja_skills/cluster_1.gif filter=lfs diff=lfs merge=lfs -text +videos/ninja_skills/cluster_2.gif filter=lfs diff=lfs merge=lfs -text +videos/ninja_skills/cluster_3.gif filter=lfs diff=lfs merge=lfs -text +videos/ninja_skills/cluster_4.gif filter=lfs diff=lfs merge=lfs -text +videos/ninja_skills/cluster_5.gif filter=lfs diff=lfs merge=lfs -text +videos/ninja_skills/cluster_6.gif filter=lfs diff=lfs merge=lfs -text +videos/ninja_skills/cluster_7.gif filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..2ec839c8b084ccd3cfcde12cc3f62c25c3642a09 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +The MIT License + +Copyright (c) 2019 OpenAI (http://openai.com) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md index 84e5b5ecc9480e2ea5d968e7c6064cd1047f5af0..ef7961eadf25829ac89c15aa1b11ac93575fb225 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,194 @@ ---- -license: mit -language: -- en -pipeline_tag: reinforcement-learning -tags: -- clustering -- interpretablity -- visualization -- https://arxiv.org/abs/2409.17411 -paper: https://arxiv.org/abs/2409.17411 ---- \ No newline at end of file +# Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clustering + +**Authors:** Liang Zhang, Justin Lieffers, Adarsh Pyarelal +**Conference:** NeurIPS 2025 Main Track +**Paper:** [arXiv:2409.17411](https://arxiv.org/abs/2409.17411) + +This repository contains the official implementation of our research on enhancing interpretability in deep reinforcement learning through semantic clustering techniques. Our work extends the [OpenAI train-procgen](https://github.com/openai/train-procgen) framework to incorporate semantic clustering methods for improved understanding and visualization of learned policies in procedural environments. + +## 📋 Abstract + +This work presents a novel approach to enhancing interpretability in deep reinforcement learning by leveraging semantic clustering techniques. We demonstrate how semantic clustering can provide insights into learned policies, enabling better understanding of agent behavior and decision-making processes in complex procedural environments. + +## 🚀 Quick Start + +### Installation + +Prerequisite: Python 3.8. + +1. **Clone the repository:** + ```bash + git clone https://github.com/ualiangzhang/semantic_rl.git + cd semantic_rl + ``` + +2. **Install dependencies (Python 3.8):** + ```bash + pip install -r requirements.txt + ``` + +3. **Install Procgen environments:** + Follow the installation steps in the [Procgen repository](https://github.com/openai/procgen). + +### Basic Usage + +**Train a semantic clustering model:** +```bash +python -m train_procgen.train_sppo --env_name --num_levels 0 --distribution_mode easy --timesteps_per_proc 25000000 --rand_seed +``` + +**Train a baseline model:** +```bash +python -m train_procgen.train_ppo --env_name --num_levels 0 --distribution_mode easy --timesteps_per_proc 25000000 --rand_seed +``` + +## 📊 Visualization and Analysis + +### Performance Analysis + +**Generate generalization figures for a single game:** +```bash +cd train_procgen +python single_graph.py --env_name +# Example: +python single_graph.py --env_name coinrun +``` + +### Semantic Clustering Visualization + +**Generate embedding space visualizations:** +```bash +python -m train_procgen.enjoy_sppo --env_name --mode 1 +``` + +**Generate skill demonstration videos:** +```bash +python -m train_procgen.enjoy_sppo --env_name --mode 0 +``` + +**Interactive cluster exploration:** +```bash +python -m train_procgen.hover_clusters --env_name +# Example: +python -m train_procgen.hover_clusters --env_name fruitbot +``` + +## 🎮 Supported Environments + +Our implementation supports four Procgen environments: +- **CoinRun** +- **FruitBot** +- **Jumper** +- **Ninja** + +## 🎬 Semantic Clustering Demonstration + +### Ninja Environment - 8 Semantic Clusters + +The following videos demonstrate the 8 distinct semantic clusters learned by our model in the Ninja environment. Each cluster represents a different behavioral pattern and skill set: + +#### 📹 Semantic Cluster Demonstrations + + + + + + + + + + + + + + +
+Cluster 0
+ +		 +Cluster 1
+ +		 +Cluster 2
+ +		 +Cluster 3
+ +
+Cluster 4
+ +		 +Cluster 5
+ +		 +Cluster 6
+ +		 +Cluster 7
+ +
+ +#### 🧭 Behavior Descriptions (Ninja) + +| Cluster | Behavior | +|---------|----------| +| 0 | The agent starts by walking through the first platform and then performs a high jump to reach a higher ledge. | +| 1 | The agent makes small jumps in the middle of the scene. | +| 2 | Two interpretations are present: (1) the agent starts from the leftmost end of the scene and walks to the starting position of Cluster 0; (2) when there are no higher ledges to jump to, the agent begins from the scene, walks over the first platform, and prepares to jump to the subsequent ledge. | +| 3 | The agent walks on the ledge and prepares to jump to a higher ledge. | +| 4 | After performing a high jump, the agent loses sight of the ledge below. | +| 5 | The agent walks on the ledge and prepares to jump onto a ledge at the same height or lower. | +| 6 | The agent executes a high jump while keeping the ledge below in sight. | +| 7 | The agent moves towards the right edge of the scene and touches the mushroom. | + +#### 📊 Alternative: Generate Your Own Videos + +You can also generate these videos yourself using our code: + +```bash +# Generate Ninja skill cluster videos +python -m train_procgen.enjoy_sppo --env_name ninja --mode 0 --num_embeddings 8 +``` + +**Note:** These videos showcase the distinct behavioral patterns learned by our semantic clustering approach. Each cluster demonstrates different combat strategies, movement patterns, and decision-making processes in the Ninja environment. + +## 📁 Output Structure + +``` +baseline/ # Required RL training package +train_procgen/ +├── checkpoints/ # Trained model checkpoints +├── figures/ # Generated visualizations and videos +videos/ # video clips corresponding to the clusters in the paper +``` + +## 📈 Reproducing Results + +To reproduce the results from our paper: + +1. **(Optional) Use existing checkpoints**: We have provided pre-trained checkpoints for Ninja, FruitBot, and Jumper (random seed 2021) in this repository under `train_procgen/checkpoints/`. You can skip training and directly run the visualization scripts. Otherwise, **train models** using the commands above. +2. **Generate visualizations** using the provided scripts +3. **Analyze results** using the interactive tools + +**Note:** Video generation may take 30-60 minutes depending on machine performance, as it ensures comprehensive exploration of all clusters. + + + + + +## 📄 License + +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. + +## 🙏 Acknowledgments + +This work builds upon the [OpenAI train-procgen](https://github.com/openai/train-procgen) framework. We thank the original authors for their excellent work on procedural generation for reinforcement learning benchmarking. \ No newline at end of file diff --git a/baselines/__init__.py b/baselines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/a2c/README.md b/baselines/a2c/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9450054021acf3af1abc8927d5c74ff162813b02 --- /dev/null +++ b/baselines/a2c/README.md @@ -0,0 +1,13 @@ +# A2C + +- Original paper: https://arxiv.org/abs/1602.01783 +- Baselines blog post: https://blog.openai.com/baselines-acktr-a2c/ +- `python -m baselines.run --alg=a2c --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options +- also refer to the repo-wide [README.md](../../README.md#training-models) + +## Files +- `run_atari`: file used to run the algorithm. +- `policies.py`: contains the different versions of the A2C architecture (MlpPolicy, CNNPolicy, LstmPolicy...). +- `a2c.py`: - Model : class used to initialize the step_model (sampling) and train_model (training) + - learn : Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm. +- `runner.py`: class used to generates a batch of experiences diff --git a/baselines/a2c/__init__.py b/baselines/a2c/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/a2c/a2c.py b/baselines/a2c/a2c.py new file mode 100644 index 0000000000000000000000000000000000000000..0947b9d35343cac3c7e7f5b95346aa03e8284dea --- /dev/null +++ b/baselines/a2c/a2c.py @@ -0,0 +1,232 @@ +import time +import functools +import tensorflow as tf + +from baselines import logger + +from baselines.common import set_global_seeds, explained_variance +from baselines.common import tf_util +from baselines.common.policies import build_policy + + +from baselines.a2c.utils import Scheduler, find_trainable_variables +from baselines.a2c.runner import Runner +from baselines.ppo2.ppo2 import safemean +from collections import deque + +from tensorflow import losses + +class Model(object): + + """ + We use this class to : + __init__: + - Creates the step_model + - Creates the train_model + + train(): + - Make the training part (feedforward and retropropagation of gradients) + + save/load(): + - Save load the model + """ + def __init__(self, policy, env, nsteps, + ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4, + alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6), lrschedule='linear'): + + sess = tf_util.get_session() + nenvs = env.num_envs + nbatch = nenvs*nsteps + + + with tf.compat.v1.variable_scope('a2c_model', reuse=tf.compat.v1.AUTO_REUSE): + # step_model is used for sampling + step_model = policy(nenvs, 1, sess) + + # train_model is used to train our network + train_model = policy(nbatch, nsteps, sess) + + A = tf.compat.v1.placeholder(train_model.action.dtype, train_model.action.shape) + ADV = tf.compat.v1.placeholder(tf.float32, [nbatch]) + R = tf.compat.v1.placeholder(tf.float32, [nbatch]) + LR = tf.compat.v1.placeholder(tf.float32, []) + + # Calculate the loss + # Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss + + # Policy loss + neglogpac = train_model.pd.neglogp(A) + # L = A(s,a) * -logpi(a|s) + pg_loss = tf.reduce_mean(input_tensor=ADV * neglogpac) + + # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy. + entropy = tf.reduce_mean(input_tensor=train_model.pd.entropy()) + + # Value loss + vf_loss = losses.mean_squared_error(tf.squeeze(train_model.vf), R) + + loss = pg_loss - entropy*ent_coef + vf_loss * vf_coef + + # Update parameters using loss + # 1. Get the model parameters + params = find_trainable_variables("a2c_model") + + # 2. Calculate the gradients + grads = tf.gradients(ys=loss, xs=params) + if max_grad_norm is not None: + # Clip the gradients (normalize) + grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm) + grads = list(zip(grads, params)) + # zip aggregate each gradient with parameters associated + # For instance zip(ABCD, xyza) => Ax, By, Cz, Da + + # 3. Make op for one policy and value update step of A2C + trainer = tf.compat.v1.train.RMSPropOptimizer(learning_rate=LR, decay=alpha, epsilon=epsilon) + + _train = trainer.apply_gradients(grads) + + lr = Scheduler(v=lr, nvalues=total_timesteps, schedule=lrschedule) + + def train(obs, states, rewards, masks, actions, values): + # Here we calculate advantage A(s,a) = R + yV(s') - V(s) + # rewards = R + yV(s') + advs = rewards - values + for step in range(len(obs)): + cur_lr = lr.value() + + td_map = {train_model.X:obs, A:actions, ADV:advs, R:rewards, LR:cur_lr} + if states is not None: + td_map[train_model.S] = states + td_map[train_model.M] = masks + policy_loss, value_loss, policy_entropy, _ = sess.run( + [pg_loss, vf_loss, entropy, _train], + td_map + ) + return policy_loss, value_loss, policy_entropy + + + self.train = train + self.train_model = train_model + self.step_model = step_model + self.step = step_model.step + self.value = step_model.value + self.initial_state = step_model.initial_state + self.save = functools.partial(tf_util.save_variables, sess=sess) + self.load = functools.partial(tf_util.load_variables, sess=sess) + tf.compat.v1.global_variables_initializer().run(session=sess) + + +def learn( + network, + env, + seed=None, + nsteps=5, + total_timesteps=int(80e6), + vf_coef=0.5, + ent_coef=0.01, + max_grad_norm=0.5, + lr=7e-4, + lrschedule='linear', + epsilon=1e-5, + alpha=0.99, + gamma=0.99, + log_interval=100, + load_path=None, + **network_kwargs): + + ''' + Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm. + + Parameters: + ----------- + + network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list) + specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns + tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward + neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets. + See baselines.common/policies.py/lstm for more details on using recurrent nets in policies + + + env: RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py) + + + seed: seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible) + + nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where + nenv is number of environment copies simulated in parallel) + + total_timesteps: int, total number of timesteps to train on (default: 80M) + + vf_coef: float, coefficient in front of value function loss in the total loss function (default: 0.5) + + ent_coef: float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01) + + max_gradient_norm: float, gradient is clipped to have global L2 norm no more than this value (default: 0.5) + + lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4) + + lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and + returns fraction of the learning rate (specified as lr) as output + + epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5) + + alpha: float, RMSProp decay parameter (default: 0.99) + + gamma: float, reward discounting parameter (default: 0.99) + + log_interval: int, specifies how frequently the logs are printed out (default: 100) + + **network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network + For instance, 'mlp' network architecture has arguments num_hidden and num_layers. + + ''' + + + + set_global_seeds(seed) + + # Get the nb of env + nenvs = env.num_envs + policy = build_policy(env, network, **network_kwargs) + + # Instantiate the model object (that creates step_model and train_model) + model = Model(policy=policy, env=env, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef, + max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule) + if load_path is not None: + model.load(load_path) + + # Instantiate the runner object + runner = Runner(env, model, nsteps=nsteps, gamma=gamma) + epinfobuf = deque(maxlen=100) + + # Calculate the batch_size + nbatch = nenvs*nsteps + + # Start total timer + tstart = time.time() + + for update in range(1, total_timesteps//nbatch+1): + # Get mini batch of experiences + obs, states, rewards, masks, actions, values, epinfos = runner.run() + epinfobuf.extend(epinfos) + + policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values) + nseconds = time.time()-tstart + + # Calculate the fps (frame per second) + fps = int((update*nbatch)/nseconds) + if update % log_interval == 0 or update == 1: + # Calculates if value function is a good predicator of the returns (ev > 1) + # or if it's just worse than predicting nothing (ev =< 0) + ev = explained_variance(values, rewards) + logger.record_tabular("nupdates", update) + logger.record_tabular("total_timesteps", update*nbatch) + logger.record_tabular("fps", fps) + logger.record_tabular("policy_entropy", float(policy_entropy)) + logger.record_tabular("value_loss", float(value_loss)) + logger.record_tabular("explained_variance", float(ev)) + logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf])) + logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf])) + logger.dump_tabular() + return model + diff --git a/baselines/a2c/runner.py b/baselines/a2c/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..c9d610a6204c9424ff14207fce67bf50d54f28e1 --- /dev/null +++ b/baselines/a2c/runner.py @@ -0,0 +1,76 @@ +import numpy as np +from baselines.a2c.utils import discount_with_dones +from baselines.common.runners import AbstractEnvRunner + +class Runner(AbstractEnvRunner): + """ + We use this class to generate batches of experiences + + __init__: + - Initialize the runner + + run(): + - Make a mini batch of experiences + """ + def __init__(self, env, model, nsteps=5, gamma=0.99): + super().__init__(env=env, model=model, nsteps=nsteps) + self.gamma = gamma + self.batch_action_shape = [x if x is not None else -1 for x in model.train_model.action.shape.as_list()] + self.ob_dtype = model.train_model.X.dtype.as_numpy_dtype + + def run(self): + # We initialize the lists that will contain the mb of experiences + mb_obs, mb_rewards, mb_actions, mb_values, mb_dones = [],[],[],[],[] + mb_states = self.states + epinfos = [] + for n in range(self.nsteps): + # Given observations, take action and value (V(s)) + # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init + actions, values, states, _ = self.model.step(self.obs, S=self.states, M=self.dones) + + # Append the experiences + mb_obs.append(np.copy(self.obs)) + mb_actions.append(actions) + mb_values.append(values) + mb_dones.append(self.dones) + + # Take actions in env and look the results + obs, rewards, dones, infos = self.env.step(actions) + for info in infos: + maybeepinfo = info.get('episode') + if maybeepinfo: epinfos.append(maybeepinfo) + self.states = states + self.dones = dones + self.obs = obs + mb_rewards.append(rewards) + mb_dones.append(self.dones) + + # Batch of steps to batch of rollouts + mb_obs = np.asarray(mb_obs, dtype=self.ob_dtype).swapaxes(1, 0).reshape(self.batch_ob_shape) + mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0) + mb_actions = np.asarray(mb_actions, dtype=self.model.train_model.action.dtype.name).swapaxes(1, 0) + mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0) + mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0) + mb_masks = mb_dones[:, :-1] + mb_dones = mb_dones[:, 1:] + + + if self.gamma > 0.0: + # Discount/bootstrap off value fn + last_values = self.model.value(self.obs, S=self.states, M=self.dones).tolist() + for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)): + rewards = rewards.tolist() + dones = dones.tolist() + if dones[-1] == 0: + rewards = discount_with_dones(rewards+[value], dones+[0], self.gamma)[:-1] + else: + rewards = discount_with_dones(rewards, dones, self.gamma) + + mb_rewards[n] = rewards + + mb_actions = mb_actions.reshape(self.batch_action_shape) + + mb_rewards = mb_rewards.flatten() + mb_values = mb_values.flatten() + mb_masks = mb_masks.flatten() + return mb_obs, mb_states, mb_rewards, mb_masks, mb_actions, mb_values, epinfos diff --git a/baselines/a2c/utils.py b/baselines/a2c/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f824831b5ab2d0cfc4e4ebdf3e5c78ef271ae8e0 --- /dev/null +++ b/baselines/a2c/utils.py @@ -0,0 +1,291 @@ +import os +import numpy as np +import tensorflow as tf +from collections import deque + +def sample(logits): + noise = tf.random.uniform(tf.shape(input=logits)) + return tf.argmax(input=logits - tf.math.log(-tf.math.log(noise)), axis=1) + +def cat_entropy(logits): + a0 = logits - tf.reduce_max(input_tensor=logits, axis=1, keepdims=True) + ea0 = tf.exp(a0) + z0 = tf.reduce_sum(input_tensor=ea0, axis=1, keepdims=True) + p0 = ea0 / z0 + return tf.reduce_sum(input_tensor=p0 * (tf.math.log(z0) - a0), axis=1) + +def cat_entropy_softmax(p0): + return - tf.reduce_sum(input_tensor=p0 * tf.math.log(p0 + 1e-6), axis = 1) + +def ortho_init(scale=1.0): + def _ortho_init(shape, dtype, partition_info=None): + #lasagne ortho init for tf + shape = tuple(shape) + if len(shape) == 2: + flat_shape = shape + elif len(shape) == 4: # assumes NHWC + flat_shape = (np.prod(shape[:-1]), shape[-1]) + else: + raise NotImplementedError + a = np.random.normal(0.0, 1.0, flat_shape) + u, _, v = np.linalg.svd(a, full_matrices=False) + q = u if u.shape == flat_shape else v # pick the one with the correct shape + q = q.reshape(shape) + return (scale * q[:shape[0], :shape[1]]).astype(np.float32) + return _ortho_init + +def conv(x, scope, *, nf, rf, stride, pad='VALID', init_scale=1.0, data_format='NHWC', one_dim_bias=False): + if data_format == 'NHWC': + channel_ax = 3 + strides = [1, stride, stride, 1] + bshape = [1, 1, 1, nf] + elif data_format == 'NCHW': + channel_ax = 1 + strides = [1, 1, stride, stride] + bshape = [1, nf, 1, 1] + else: + raise NotImplementedError + bias_var_shape = [nf] if one_dim_bias else [1, nf, 1, 1] + try: + nin = x.get_shape()[channel_ax].value + except: + nin = x.get_shape()[channel_ax] + wshape = [rf, rf, nin, nf] + with tf.compat.v1.variable_scope(scope): + w = tf.compat.v1.get_variable("w", wshape, initializer=ortho_init(init_scale)) + b = tf.compat.v1.get_variable("b", bias_var_shape, initializer=tf.compat.v1.constant_initializer(0.0)) + if not one_dim_bias and data_format == 'NHWC': + b = tf.reshape(b, bshape) + return tf.nn.conv2d(input=x, filters=w, strides=strides, padding=pad, data_format=data_format) + b + +def fc(x, scope, nh, *, init_scale=1.0, init_bias=0.0): + with tf.compat.v1.variable_scope(scope): + try: + nin = x.get_shape()[1].value + except: + nin = x.get_shape()[1] + w = tf.compat.v1.get_variable("w", [nin, nh], initializer=ortho_init(init_scale)) + b = tf.compat.v1.get_variable("b", [nh], initializer=tf.compat.v1.constant_initializer(init_bias)) + return tf.matmul(x, w)+b + +def batch_to_seq(h, nbatch, nsteps, flat=False): + if flat: + h = tf.reshape(h, [nbatch, nsteps]) + else: + h = tf.reshape(h, [nbatch, nsteps, -1]) + return [tf.squeeze(v, [1]) for v in tf.split(axis=1, num_or_size_splits=nsteps, value=h)] + +def seq_to_batch(h, flat = False): + shape = h[0].get_shape().as_list() + if not flat: + assert(len(shape) > 1) + nh = h[0].get_shape()[-1].value + return tf.reshape(tf.concat(axis=1, values=h), [-1, nh]) + else: + return tf.reshape(tf.stack(values=h, axis=1), [-1]) + +def lstm(xs, ms, s, scope, nh, init_scale=1.0): + nbatch, nin = [v.value for v in xs[0].get_shape()] + with tf.compat.v1.variable_scope(scope): + wx = tf.compat.v1.get_variable("wx", [nin, nh*4], initializer=ortho_init(init_scale)) + wh = tf.compat.v1.get_variable("wh", [nh, nh*4], initializer=ortho_init(init_scale)) + b = tf.compat.v1.get_variable("b", [nh*4], initializer=tf.compat.v1.constant_initializer(0.0)) + + c, h = tf.split(axis=1, num_or_size_splits=2, value=s) + for idx, (x, m) in enumerate(zip(xs, ms)): + c = c*(1-m) + h = h*(1-m) + z = tf.matmul(x, wx) + tf.matmul(h, wh) + b + i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z) + i = tf.nn.sigmoid(i) + f = tf.nn.sigmoid(f) + o = tf.nn.sigmoid(o) + u = tf.tanh(u) + c = f*c + i*u + h = o*tf.tanh(c) + xs[idx] = h + s = tf.concat(axis=1, values=[c, h]) + return xs, s + +def _ln(x, g, b, e=1e-5, axes=[1]): + u, s = tf.nn.moments(x=x, axes=axes, keepdims=True) + x = (x-u)/tf.sqrt(s+e) + x = x*g+b + return x + +def lnlstm(xs, ms, s, scope, nh, init_scale=1.0): + nbatch, nin = [v.value for v in xs[0].get_shape()] + with tf.compat.v1.variable_scope(scope): + wx = tf.compat.v1.get_variable("wx", [nin, nh*4], initializer=ortho_init(init_scale)) + gx = tf.compat.v1.get_variable("gx", [nh*4], initializer=tf.compat.v1.constant_initializer(1.0)) + bx = tf.compat.v1.get_variable("bx", [nh*4], initializer=tf.compat.v1.constant_initializer(0.0)) + + wh = tf.compat.v1.get_variable("wh", [nh, nh*4], initializer=ortho_init(init_scale)) + gh = tf.compat.v1.get_variable("gh", [nh*4], initializer=tf.compat.v1.constant_initializer(1.0)) + bh = tf.compat.v1.get_variable("bh", [nh*4], initializer=tf.compat.v1.constant_initializer(0.0)) + + b = tf.compat.v1.get_variable("b", [nh*4], initializer=tf.compat.v1.constant_initializer(0.0)) + + gc = tf.compat.v1.get_variable("gc", [nh], initializer=tf.compat.v1.constant_initializer(1.0)) + bc = tf.compat.v1.get_variable("bc", [nh], initializer=tf.compat.v1.constant_initializer(0.0)) + + c, h = tf.split(axis=1, num_or_size_splits=2, value=s) + for idx, (x, m) in enumerate(zip(xs, ms)): + c = c*(1-m) + h = h*(1-m) + z = _ln(tf.matmul(x, wx), gx, bx) + _ln(tf.matmul(h, wh), gh, bh) + b + i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z) + i = tf.nn.sigmoid(i) + f = tf.nn.sigmoid(f) + o = tf.nn.sigmoid(o) + u = tf.tanh(u) + c = f*c + i*u + h = o*tf.tanh(_ln(c, gc, bc)) + xs[idx] = h + s = tf.concat(axis=1, values=[c, h]) + return xs, s + +def conv_to_fc(x): + try: + nh = np.prod([v.value for v in x.get_shape()[1:]]) + except: + nh = np.prod([v for v in x.get_shape()[1:]]) + x = tf.reshape(x, [-1, nh]) + return x + +def discount_with_dones(rewards, dones, gamma): + discounted = [] + r = 0 + for reward, done in zip(rewards[::-1], dones[::-1]): + r = reward + gamma*r*(1.-done) # fixed off by one bug + discounted.append(r) + return discounted[::-1] + +def find_trainable_variables(key): + return tf.compat.v1.trainable_variables(key) + +def make_path(f): + return os.makedirs(f, exist_ok=True) + +def constant(p): + return 1 + +def linear(p): + return 1-p + +def middle_drop(p): + eps = 0.75 + if 1-p [0..1] that takes fraction of the training progress as input and + returns fraction of the learning rate (specified as lr) as output + + rprop_epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5) + + rprop_alpha: float, RMSProp decay parameter (default: 0.99) + + gamma: float, reward discounting factor (default: 0.99) + + log_interval: int, number of updates between logging events (default: 100) + + buffer_size: int, size of the replay buffer (default: 50k) + + replay_ratio: int, now many (on average) batches of data to sample from the replay buffer take after batch from the environment (default: 4) + + replay_start: int, the sampling from the replay buffer does not start until replay buffer has at least that many samples (default: 10k) + + c: float, importance weight clipping factor (default: 10) + + trust_region bool, whether or not algorithms estimates the gradient KL divergence between the old and updated policy and uses it to determine step size (default: True) + + delta: float, max KL divergence between the old policy and updated policy (default: 1) + + alpha: float, momentum factor in the Polyak (exponential moving average) averaging of the model parameters (default: 0.99) + + load_path: str, path to load the model from (default: None) + + **network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network + For instance, 'mlp' network architecture has arguments num_hidden and num_layers. + + ''' + + print("Running Acer Simple") + print(locals()) + set_global_seeds(seed) + if not isinstance(env, VecFrameStack): + env = VecFrameStack(env, 1) + + policy = build_policy(env, network, estimate_q=True, **network_kwargs) + nenvs = env.num_envs + ob_space = env.observation_space + ac_space = env.action_space + + nstack = env.nstack + model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps, + ent_coef=ent_coef, q_coef=q_coef, gamma=gamma, + max_grad_norm=max_grad_norm, lr=lr, rprop_alpha=rprop_alpha, rprop_epsilon=rprop_epsilon, + total_timesteps=total_timesteps, lrschedule=lrschedule, c=c, + trust_region=trust_region, alpha=alpha, delta=delta) + + if load_path is not None: + model.load(load_path) + + runner = Runner(env=env, model=model, nsteps=nsteps) + if replay_ratio > 0: + buffer = Buffer(env=env, nsteps=nsteps, size=buffer_size) + else: + buffer = None + nbatch = nenvs*nsteps + acer = Acer(runner, model, buffer, log_interval) + acer.tstart = time.time() + + for acer.steps in range(0, total_timesteps, nbatch): #nbatch samples, 1 on_policy call and multiple off-policy calls + acer.call(on_policy=True) + if replay_ratio > 0 and buffer.has_atleast(replay_start): + n = np.random.poisson(replay_ratio) + for _ in range(n): + acer.call(on_policy=False) # no simulation steps in this + + return model diff --git a/baselines/acer/buffer.py b/baselines/acer/buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..000592cc8d28cb6a5494ef1393acca9dc76bad1a --- /dev/null +++ b/baselines/acer/buffer.py @@ -0,0 +1,156 @@ +import numpy as np + +class Buffer(object): + # gets obs, actions, rewards, mu's, (states, masks), dones + def __init__(self, env, nsteps, size=50000): + self.nenv = env.num_envs + self.nsteps = nsteps + # self.nh, self.nw, self.nc = env.observation_space.shape + self.obs_shape = env.observation_space.shape + self.obs_dtype = env.observation_space.dtype + self.ac_dtype = env.action_space.dtype + self.nc = self.obs_shape[-1] + self.nstack = env.nstack + self.nc //= self.nstack + self.nbatch = self.nenv * self.nsteps + self.size = size // (self.nsteps) # Each loc contains nenv * nsteps frames, thus total buffer is nenv * size frames + + # Memory + self.enc_obs = None + self.actions = None + self.rewards = None + self.mus = None + self.dones = None + self.masks = None + + # Size indexes + self.next_idx = 0 + self.num_in_buffer = 0 + + def has_atleast(self, frames): + # Frames per env, so total (nenv * frames) Frames needed + # Each buffer loc has nenv * nsteps frames + return self.num_in_buffer >= (frames // self.nsteps) + + def can_sample(self): + return self.num_in_buffer > 0 + + # Generate stacked frames + def decode(self, enc_obs, dones): + # enc_obs has shape [nenvs, nsteps + nstack, nh, nw, nc] + # dones has shape [nenvs, nsteps] + # returns stacked obs of shape [nenv, (nsteps + 1), nh, nw, nstack*nc] + + return _stack_obs(enc_obs, dones, + nsteps=self.nsteps) + + def put(self, enc_obs, actions, rewards, mus, dones, masks): + # enc_obs [nenv, (nsteps + nstack), nh, nw, nc] + # actions, rewards, dones [nenv, nsteps] + # mus [nenv, nsteps, nact] + + if self.enc_obs is None: + self.enc_obs = np.empty([self.size] + list(enc_obs.shape), dtype=self.obs_dtype) + self.actions = np.empty([self.size] + list(actions.shape), dtype=self.ac_dtype) + self.rewards = np.empty([self.size] + list(rewards.shape), dtype=np.float32) + self.mus = np.empty([self.size] + list(mus.shape), dtype=np.float32) + self.dones = np.empty([self.size] + list(dones.shape), dtype=np.bool) + self.masks = np.empty([self.size] + list(masks.shape), dtype=np.bool) + + self.enc_obs[self.next_idx] = enc_obs + self.actions[self.next_idx] = actions + self.rewards[self.next_idx] = rewards + self.mus[self.next_idx] = mus + self.dones[self.next_idx] = dones + self.masks[self.next_idx] = masks + + self.next_idx = (self.next_idx + 1) % self.size + self.num_in_buffer = min(self.size, self.num_in_buffer + 1) + + def take(self, x, idx, envx): + nenv = self.nenv + out = np.empty([nenv] + list(x.shape[2:]), dtype=x.dtype) + for i in range(nenv): + out[i] = x[idx[i], envx[i]] + return out + + def get(self): + # returns + # obs [nenv, (nsteps + 1), nh, nw, nstack*nc] + # actions, rewards, dones [nenv, nsteps] + # mus [nenv, nsteps, nact] + nenv = self.nenv + assert self.can_sample() + + # Sample exactly one id per env. If you sample across envs, then higher correlation in samples from same env. + idx = np.random.randint(0, self.num_in_buffer, nenv) + envx = np.arange(nenv) + + take = lambda x: self.take(x, idx, envx) # for i in range(nenv)], axis = 0) + dones = take(self.dones) + enc_obs = take(self.enc_obs) + obs = self.decode(enc_obs, dones) + actions = take(self.actions) + rewards = take(self.rewards) + mus = take(self.mus) + masks = take(self.masks) + return obs, actions, rewards, mus, dones, masks + + + +def _stack_obs_ref(enc_obs, dones, nsteps): + nenv = enc_obs.shape[0] + nstack = enc_obs.shape[1] - nsteps + nh, nw, nc = enc_obs.shape[2:] + obs_dtype = enc_obs.dtype + obs_shape = (nh, nw, nc*nstack) + + mask = np.empty([nsteps + nstack - 1, nenv, 1, 1, 1], dtype=np.float32) + obs = np.zeros([nstack, nsteps + nstack, nenv, nh, nw, nc], dtype=obs_dtype) + x = np.reshape(enc_obs, [nenv, nsteps + nstack, nh, nw, nc]).swapaxes(1, 0) # [nsteps + nstack, nenv, nh, nw, nc] + + mask[nstack-1:] = np.reshape(1.0 - dones, [nenv, nsteps, 1, 1, 1]).swapaxes(1, 0) # keep + mask[:nstack-1] = 1.0 + + # y = np.reshape(1 - dones, [nenvs, nsteps, 1, 1, 1]) + for i in range(nstack): + obs[-(i + 1), i:] = x + # obs[:,i:,:,:,-(i+1),:] = x + x = x[:-1] * mask + mask = mask[1:] + + return np.reshape(obs[:, (nstack-1):].transpose((2, 1, 3, 4, 0, 5)), (nenv, (nsteps + 1)) + obs_shape) + +def _stack_obs(enc_obs, dones, nsteps): + nenv = enc_obs.shape[0] + nstack = enc_obs.shape[1] - nsteps + nc = enc_obs.shape[-1] + + obs_ = np.zeros((nenv, nsteps + 1) + enc_obs.shape[2:-1] + (enc_obs.shape[-1] * nstack, ), dtype=enc_obs.dtype) + mask = np.ones((nenv, nsteps+1), dtype=enc_obs.dtype) + mask[:, 1:] = 1.0 - dones + mask = mask.reshape(mask.shape + tuple(np.ones(len(enc_obs.shape)-2, dtype=np.uint8))) + + for i in range(nstack-1, -1, -1): + obs_[..., i * nc : (i + 1) * nc] = enc_obs[:, i : i + nsteps + 1, :] + if i < nstack-1: + obs_[..., i * nc : (i + 1) * nc] *= mask + mask[:, 1:, ...] *= mask[:, :-1, ...] + + return obs_ + +def test_stack_obs(): + nstack = 7 + nenv = 1 + nsteps = 5 + + obs_shape = (2, 3, nstack) + + enc_obs_shape = (nenv, nsteps + nstack) + obs_shape[:-1] + (1,) + enc_obs = np.random.random(enc_obs_shape) + dones = np.random.randint(low=0, high=2, size=(nenv, nsteps)) + + stacked_obs_ref = _stack_obs_ref(enc_obs, dones, nsteps=nsteps) + stacked_obs_test = _stack_obs(enc_obs, dones, nsteps=nsteps) + + np.testing.assert_allclose(stacked_obs_ref, stacked_obs_test) diff --git a/baselines/acer/defaults.py b/baselines/acer/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..0334baea935a5b1b759a4ef11c3b5e5e8773b4b0 --- /dev/null +++ b/baselines/acer/defaults.py @@ -0,0 +1,4 @@ +def atari(): + return dict( + lrschedule='constant' + ) diff --git a/baselines/acer/policies.py b/baselines/acer/policies.py new file mode 100644 index 0000000000000000000000000000000000000000..14440d7ad77415528e4dfe094bd5a1770260fe73 --- /dev/null +++ b/baselines/acer/policies.py @@ -0,0 +1,81 @@ +import numpy as np +import tensorflow as tf +from baselines.common.policies import nature_cnn +from baselines.a2c.utils import fc, batch_to_seq, seq_to_batch, lstm, sample + + +class AcerCnnPolicy(object): + + def __init__(self, sess, ob_space, ac_space, nenv, nsteps, nstack, reuse=False): + nbatch = nenv * nsteps + nh, nw, nc = ob_space.shape + ob_shape = (nbatch, nh, nw, nc * nstack) + nact = ac_space.n + X = tf.compat.v1.placeholder(tf.uint8, ob_shape) # obs + with tf.compat.v1.variable_scope("model", reuse=reuse): + h = nature_cnn(X) + pi_logits = fc(h, 'pi', nact, init_scale=0.01) + pi = tf.nn.softmax(pi_logits) + q = fc(h, 'q', nact) + + a = sample(tf.nn.softmax(pi_logits)) # could change this to use self.pi instead + self.initial_state = [] # not stateful + self.X = X + self.pi = pi # actual policy params now + self.pi_logits = pi_logits + self.q = q + self.vf = q + + def step(ob, *args, **kwargs): + # returns actions, mus, states + a0, pi0 = sess.run([a, pi], {X: ob}) + return a0, pi0, [] # dummy state + + def out(ob, *args, **kwargs): + pi0, q0 = sess.run([pi, q], {X: ob}) + return pi0, q0 + + def act(ob, *args, **kwargs): + return sess.run(a, {X: ob}) + + self.step = step + self.out = out + self.act = act + +class AcerLstmPolicy(object): + + def __init__(self, sess, ob_space, ac_space, nenv, nsteps, nstack, reuse=False, nlstm=256): + nbatch = nenv * nsteps + nh, nw, nc = ob_space.shape + ob_shape = (nbatch, nh, nw, nc * nstack) + nact = ac_space.n + X = tf.compat.v1.placeholder(tf.uint8, ob_shape) # obs + M = tf.compat.v1.placeholder(tf.float32, [nbatch]) #mask (done t-1) + S = tf.compat.v1.placeholder(tf.float32, [nenv, nlstm*2]) #states + with tf.compat.v1.variable_scope("model", reuse=reuse): + h = nature_cnn(X) + + # lstm + xs = batch_to_seq(h, nenv, nsteps) + ms = batch_to_seq(M, nenv, nsteps) + h5, snew = lstm(xs, ms, S, 'lstm1', nh=nlstm) + h5 = seq_to_batch(h5) + + pi_logits = fc(h5, 'pi', nact, init_scale=0.01) + pi = tf.nn.softmax(pi_logits) + q = fc(h5, 'q', nact) + + a = sample(pi_logits) # could change this to use self.pi instead + self.initial_state = np.zeros((nenv, nlstm*2), dtype=np.float32) + self.X = X + self.M = M + self.S = S + self.pi = pi # actual policy params now + self.q = q + + def step(ob, state, mask, *args, **kwargs): + # returns actions, mus, states + a0, pi0, s = sess.run([a, pi, snew], {X: ob, S: state, M: mask}) + return a0, pi0, s + + self.step = step diff --git a/baselines/acer/runner.py b/baselines/acer/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..afd19ce2eb531e2ae76838205e6b91432ccca215 --- /dev/null +++ b/baselines/acer/runner.py @@ -0,0 +1,61 @@ +import numpy as np +from baselines.common.runners import AbstractEnvRunner +from baselines.common.vec_env.vec_frame_stack import VecFrameStack +from gym import spaces + + +class Runner(AbstractEnvRunner): + + def __init__(self, env, model, nsteps): + super().__init__(env=env, model=model, nsteps=nsteps) + assert isinstance(env.action_space, spaces.Discrete), 'This ACER implementation works only with discrete action spaces!' + assert isinstance(env, VecFrameStack) + + self.nact = env.action_space.n + nenv = self.nenv + self.nbatch = nenv * nsteps + self.batch_ob_shape = (nenv*(nsteps+1),) + env.observation_space.shape + + self.obs = env.reset() + self.obs_dtype = env.observation_space.dtype + self.ac_dtype = env.action_space.dtype + self.nstack = self.env.nstack + self.nc = self.batch_ob_shape[-1] // self.nstack + + + def run(self): + # enc_obs = np.split(self.obs, self.nstack, axis=3) # so now list of obs steps + enc_obs = np.split(self.env.stackedobs, self.env.nstack, axis=-1) + mb_obs, mb_actions, mb_mus, mb_dones, mb_rewards = [], [], [], [], [] + for _ in range(self.nsteps): + actions, mus, states = self.model._step(self.obs, S=self.states, M=self.dones) + mb_obs.append(np.copy(self.obs)) + mb_actions.append(actions) + mb_mus.append(mus) + mb_dones.append(self.dones) + obs, rewards, dones, _ = self.env.step(actions) + # states information for statefull models like LSTM + self.states = states + self.dones = dones + self.obs = obs + mb_rewards.append(rewards) + enc_obs.append(obs[..., -self.nc:]) + mb_obs.append(np.copy(self.obs)) + mb_dones.append(self.dones) + + enc_obs = np.asarray(enc_obs, dtype=self.obs_dtype).swapaxes(1, 0) + mb_obs = np.asarray(mb_obs, dtype=self.obs_dtype).swapaxes(1, 0) + mb_actions = np.asarray(mb_actions, dtype=self.ac_dtype).swapaxes(1, 0) + mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0) + mb_mus = np.asarray(mb_mus, dtype=np.float32).swapaxes(1, 0) + + mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0) + + mb_masks = mb_dones # Used for statefull models like LSTM's to mask state when done + mb_dones = mb_dones[:, 1:] # Used for calculating returns. The dones array is now aligned with rewards + + # shapes are now [nenv, nsteps, []] + # When pulling from buffer, arrays will now be reshaped in place, preventing a deep copy. + + return enc_obs, mb_obs, mb_actions, mb_rewards, mb_mus, mb_dones, mb_masks + diff --git a/baselines/acktr/README.md b/baselines/acktr/README.md new file mode 100644 index 0000000000000000000000000000000000000000..99f50f2bd991138893d73e8ee6290679a524743d --- /dev/null +++ b/baselines/acktr/README.md @@ -0,0 +1,9 @@ +# ACKTR + +- Original paper: https://arxiv.org/abs/1708.05144 +- Baselines blog post: https://blog.openai.com/baselines-acktr-a2c/ +- `python -m baselines.run --alg=acktr --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options. +- also refer to the repo-wide [README.md](../../README.md#training-models) + +## ACKTR with continuous action spaces +The code of ACKTR has been refactored to handle both discrete and continuous action spaces uniformly. In the original version, discrete and continuous action spaces were handled by different code (actkr_disc.py and acktr_cont.py) with little overlap. If interested in the original version of the acktr for continuous action spaces, use `old_acktr_cont` branch. Note that original code performs better on the mujoco tasks than the refactored version; we are still investigating why. diff --git a/baselines/acktr/__init__.py b/baselines/acktr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/acktr/acktr.py b/baselines/acktr/acktr.py new file mode 100644 index 0000000000000000000000000000000000000000..512dedd9f3eecb8ceab9d9b7068f330545d476c2 --- /dev/null +++ b/baselines/acktr/acktr.py @@ -0,0 +1,158 @@ +import os.path as osp +import time +import functools +import tensorflow as tf +from baselines import logger + +from baselines.common import set_global_seeds, explained_variance +from baselines.common.policies import build_policy +from baselines.common.tf_util import get_session, save_variables, load_variables + +from baselines.a2c.runner import Runner +from baselines.a2c.utils import Scheduler, find_trainable_variables +from baselines.acktr import kfac +from baselines.ppo2.ppo2 import safemean +from collections import deque + + +class Model(object): + + def __init__(self, policy, ob_space, ac_space, nenvs,total_timesteps, nprocs=32, nsteps=20, + ent_coef=0.01, vf_coef=0.5, vf_fisher_coef=1.0, lr=0.25, max_grad_norm=0.5, + kfac_clip=0.001, lrschedule='linear', is_async=True): + + self.sess = sess = get_session() + nbatch = nenvs * nsteps + with tf.compat.v1.variable_scope('acktr_model', reuse=tf.compat.v1.AUTO_REUSE): + self.model = step_model = policy(nenvs, 1, sess=sess) + self.model2 = train_model = policy(nenvs*nsteps, nsteps, sess=sess) + + A = train_model.pdtype.sample_placeholder([None]) + ADV = tf.compat.v1.placeholder(tf.float32, [nbatch]) + R = tf.compat.v1.placeholder(tf.float32, [nbatch]) + PG_LR = tf.compat.v1.placeholder(tf.float32, []) + VF_LR = tf.compat.v1.placeholder(tf.float32, []) + + neglogpac = train_model.pd.neglogp(A) + self.logits = train_model.pi + + ##training loss + pg_loss = tf.reduce_mean(input_tensor=ADV*neglogpac) + entropy = tf.reduce_mean(input_tensor=train_model.pd.entropy()) + pg_loss = pg_loss - ent_coef * entropy + vf_loss = tf.compat.v1.losses.mean_squared_error(tf.squeeze(train_model.vf), R) + train_loss = pg_loss + vf_coef * vf_loss + + + ##Fisher loss construction + self.pg_fisher = pg_fisher_loss = -tf.reduce_mean(input_tensor=neglogpac) + sample_net = train_model.vf + tf.random.normal(tf.shape(input=train_model.vf)) + self.vf_fisher = vf_fisher_loss = - vf_fisher_coef*tf.reduce_mean(input_tensor=tf.pow(train_model.vf - tf.stop_gradient(sample_net), 2)) + self.joint_fisher = joint_fisher_loss = pg_fisher_loss + vf_fisher_loss + + self.params=params = find_trainable_variables("acktr_model") + + self.grads_check = grads = tf.gradients(ys=train_loss,xs=params) + + with tf.device('/gpu:0'): + self.optim = optim = kfac.KfacOptimizer(learning_rate=PG_LR, clip_kl=kfac_clip,\ + momentum=0.9, kfac_update=1, epsilon=0.01,\ + stats_decay=0.99, is_async=is_async, cold_iter=10, max_grad_norm=max_grad_norm) + + # update_stats_op = optim.compute_and_apply_stats(joint_fisher_loss, var_list=params) + optim.compute_and_apply_stats(joint_fisher_loss, var_list=params) + train_op, q_runner = optim.apply_gradients(list(zip(grads,params))) + self.q_runner = q_runner + self.lr = Scheduler(v=lr, nvalues=total_timesteps, schedule=lrschedule) + + def train(obs, states, rewards, masks, actions, values): + advs = rewards - values + for step in range(len(obs)): + cur_lr = self.lr.value() + + td_map = {train_model.X:obs, A:actions, ADV:advs, R:rewards, PG_LR:cur_lr, VF_LR:cur_lr} + if states is not None: + td_map[train_model.S] = states + td_map[train_model.M] = masks + + policy_loss, value_loss, policy_entropy, _ = sess.run( + [pg_loss, vf_loss, entropy, train_op], + td_map + ) + return policy_loss, value_loss, policy_entropy + + + self.train = train + self.save = functools.partial(save_variables, sess=sess) + self.load = functools.partial(load_variables, sess=sess) + self.train_model = train_model + self.step_model = step_model + self.step = step_model.step + self.value = step_model.value + self.initial_state = step_model.initial_state + tf.compat.v1.global_variables_initializer().run(session=sess) + +def learn(network, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval=100, nprocs=32, nsteps=20, + ent_coef=0.01, vf_coef=0.5, vf_fisher_coef=1.0, lr=0.25, max_grad_norm=0.5, + kfac_clip=0.001, save_interval=None, lrschedule='linear', load_path=None, is_async=True, **network_kwargs): + set_global_seeds(seed) + + + if network == 'cnn': + network_kwargs['one_dim_bias'] = True + + policy = build_policy(env, network, **network_kwargs) + + nenvs = env.num_envs + ob_space = env.observation_space + ac_space = env.action_space + make_model = lambda : Model(policy, ob_space, ac_space, nenvs, total_timesteps, nprocs=nprocs, nsteps + =nsteps, ent_coef=ent_coef, vf_coef=vf_coef, vf_fisher_coef= + vf_fisher_coef, lr=lr, max_grad_norm=max_grad_norm, kfac_clip=kfac_clip, + lrschedule=lrschedule, is_async=is_async) + if save_interval and logger.get_dir(): + import cloudpickle + with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh: + fh.write(cloudpickle.dumps(make_model)) + model = make_model() + + if load_path is not None: + model.load(load_path) + + runner = Runner(env, model, nsteps=nsteps, gamma=gamma) + epinfobuf = deque(maxlen=100) + nbatch = nenvs*nsteps + tstart = time.time() + coord = tf.train.Coordinator() + if is_async: + enqueue_threads = model.q_runner.create_threads(model.sess, coord=coord, start=True) + else: + enqueue_threads = [] + + for update in range(1, total_timesteps//nbatch+1): + obs, states, rewards, masks, actions, values, epinfos = runner.run() + epinfobuf.extend(epinfos) + policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values) + model.old_obs = obs + nseconds = time.time()-tstart + fps = int((update*nbatch)/nseconds) + if update % log_interval == 0 or update == 1: + ev = explained_variance(values, rewards) + logger.record_tabular("nupdates", update) + logger.record_tabular("total_timesteps", update*nbatch) + logger.record_tabular("fps", fps) + logger.record_tabular("policy_entropy", float(policy_entropy)) + logger.record_tabular("policy_loss", float(policy_loss)) + logger.record_tabular("value_loss", float(value_loss)) + logger.record_tabular("explained_variance", float(ev)) + logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf])) + logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf])) + logger.dump_tabular() + + if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir(): + savepath = osp.join(logger.get_dir(), 'checkpoint%.5i'%update) + print('Saving to', savepath) + model.save(savepath) + coord.request_stop() + coord.join(enqueue_threads) + return model diff --git a/baselines/acktr/defaults.py b/baselines/acktr/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..f1e3ab66596e2914a279000c8d1f3a4469029b71 --- /dev/null +++ b/baselines/acktr/defaults.py @@ -0,0 +1,5 @@ +def mujoco(): + return dict( + nsteps=2500, + value_network='copy' + ) diff --git a/baselines/acktr/kfac.py b/baselines/acktr/kfac.py new file mode 100644 index 0000000000000000000000000000000000000000..50042fe8338bc8a30aaa4f7740b020aecdf6d613 --- /dev/null +++ b/baselines/acktr/kfac.py @@ -0,0 +1,928 @@ +import tensorflow as tf +import numpy as np +import re + + # flake8: noqa F403, F405 +from baselines.acktr.kfac_utils import * +from functools import reduce + +KFAC_OPS = ['MatMul', 'Conv2D', 'BiasAdd'] +KFAC_DEBUG = False + + +class KfacOptimizer(): + # note that KfacOptimizer will be truly synchronous (and thus deterministic) only if a single-threaded session is used + def __init__(self, learning_rate=0.01, momentum=0.9, clip_kl=0.01, kfac_update=2, stats_accum_iter=60, full_stats_init=False, cold_iter=100, cold_lr=None, is_async=False, async_stats=False, epsilon=1e-2, stats_decay=0.95, blockdiag_bias=False, channel_fac=False, factored_damping=False, approxT2=False, use_float64=False, weight_decay_dict={},max_grad_norm=0.5): + self.max_grad_norm = max_grad_norm + self._lr = learning_rate + self._momentum = momentum + self._clip_kl = clip_kl + self._channel_fac = channel_fac + self._kfac_update = kfac_update + self._async = is_async + self._async_stats = async_stats + self._epsilon = epsilon + self._stats_decay = stats_decay + self._blockdiag_bias = blockdiag_bias + self._approxT2 = approxT2 + self._use_float64 = use_float64 + self._factored_damping = factored_damping + self._cold_iter = cold_iter + if cold_lr == None: + # good heuristics + self._cold_lr = self._lr# * 3. + else: + self._cold_lr = cold_lr + self._stats_accum_iter = stats_accum_iter + self._weight_decay_dict = weight_decay_dict + self._diag_init_coeff = 0. + self._full_stats_init = full_stats_init + if not self._full_stats_init: + self._stats_accum_iter = self._cold_iter + + self.sgd_step = tf.Variable(0, name='KFAC/sgd_step', trainable=False) + self.global_step = tf.Variable( + 0, name='KFAC/global_step', trainable=False) + self.cold_step = tf.Variable(0, name='KFAC/cold_step', trainable=False) + self.factor_step = tf.Variable( + 0, name='KFAC/factor_step', trainable=False) + self.stats_step = tf.Variable( + 0, name='KFAC/stats_step', trainable=False) + self.vFv = tf.Variable(0., name='KFAC/vFv', trainable=False) + + self.factors = {} + self.param_vars = [] + self.stats = {} + self.stats_eigen = {} + + def getFactors(self, g, varlist): + graph = tf.compat.v1.get_default_graph() + factorTensors = {} + fpropTensors = [] + bpropTensors = [] + opTypes = [] + fops = [] + + def searchFactors(gradient, graph): + # hard coded search stratergy + bpropOp = gradient.op + bpropOp_name = bpropOp.name + + bTensors = [] + fTensors = [] + + # combining additive gradient, assume they are the same op type and + # indepedent + if 'AddN' in bpropOp_name: + factors = [] + for g in gradient.op.inputs: + factors.append(searchFactors(g, graph)) + op_names = [item['opName'] for item in factors] + # TO-DO: need to check all the attribute of the ops as well + print (gradient.name) + print (op_names) + print (len(np.unique(op_names))) + assert len(np.unique(op_names)) == 1, gradient.name + \ + ' is shared among different computation OPs' + + bTensors = reduce(lambda x, y: x + y, + [item['bpropFactors'] for item in factors]) + if len(factors[0]['fpropFactors']) > 0: + fTensors = reduce( + lambda x, y: x + y, [item['fpropFactors'] for item in factors]) + fpropOp_name = op_names[0] + fpropOp = factors[0]['op'] + else: + fpropOp_name = re.search( + 'gradientsSampled(_[0-9]+|)/(.+?)_grad', bpropOp_name).group(2) + fpropOp = graph.get_operation_by_name(fpropOp_name) + if fpropOp.op_def.name in KFAC_OPS: + # Known OPs + ### + bTensor = [ + i for i in bpropOp.inputs if 'gradientsSampled' in i.name][-1] + bTensorShape = fpropOp.outputs[0].get_shape() + if bTensor.get_shape()[0].value == None: + bTensor.set_shape(bTensorShape) + bTensors.append(bTensor) + ### + if fpropOp.op_def.name == 'BiasAdd': + fTensors = [] + else: + fTensors.append( + [i for i in fpropOp.inputs if param.op.name not in i.name][0]) + fpropOp_name = fpropOp.op_def.name + else: + # unknown OPs, block approximation used + bInputsList = [i for i in bpropOp.inputs[ + 0].op.inputs if 'gradientsSampled' in i.name if 'Shape' not in i.name] + if len(bInputsList) > 0: + bTensor = bInputsList[0] + bTensorShape = fpropOp.outputs[0].get_shape() + if len(bTensor.get_shape()) > 0 and bTensor.get_shape()[0].value == None: + bTensor.set_shape(bTensorShape) + bTensors.append(bTensor) + fpropOp_name = opTypes.append('UNK-' + fpropOp.op_def.name) + + return {'opName': fpropOp_name, 'op': fpropOp, 'fpropFactors': fTensors, 'bpropFactors': bTensors} + + for t, param in zip(g, varlist): + if KFAC_DEBUG: + print(('get factor for '+param.name)) + factors = searchFactors(t, graph) + factorTensors[param] = factors + + ######## + # check associated weights and bias for homogeneous coordinate representation + # and check redundent factors + # TO-DO: there may be a bug to detect associate bias and weights for + # forking layer, e.g. in inception models. + for param in varlist: + factorTensors[param]['assnWeights'] = None + factorTensors[param]['assnBias'] = None + for param in varlist: + if factorTensors[param]['opName'] == 'BiasAdd': + factorTensors[param]['assnWeights'] = None + for item in varlist: + if len(factorTensors[item]['bpropFactors']) > 0: + if (set(factorTensors[item]['bpropFactors']) == set(factorTensors[param]['bpropFactors'])) and (len(factorTensors[item]['fpropFactors']) > 0): + factorTensors[param]['assnWeights'] = item + factorTensors[item]['assnBias'] = param + factorTensors[param]['bpropFactors'] = factorTensors[ + item]['bpropFactors'] + + ######## + + ######## + # concatenate the additive gradients along the batch dimension, i.e. + # assuming independence structure + for key in ['fpropFactors', 'bpropFactors']: + for i, param in enumerate(varlist): + if len(factorTensors[param][key]) > 0: + if (key + '_concat') not in factorTensors[param]: + name_scope = factorTensors[param][key][0].name.split(':')[ + 0] + with tf.compat.v1.name_scope(name_scope): + factorTensors[param][ + key + '_concat'] = tf.concat(factorTensors[param][key], 0) + else: + factorTensors[param][key + '_concat'] = None + for j, param2 in enumerate(varlist[(i + 1):]): + if (len(factorTensors[param][key]) > 0) and (set(factorTensors[param2][key]) == set(factorTensors[param][key])): + factorTensors[param2][key] = factorTensors[param][key] + factorTensors[param2][ + key + '_concat'] = factorTensors[param][key + '_concat'] + ######## + + if KFAC_DEBUG: + for items in zip(varlist, fpropTensors, bpropTensors, opTypes): + print((items[0].name, factorTensors[item])) + self.factors = factorTensors + return factorTensors + + def getStats(self, factors, varlist): + if len(self.stats) == 0: + # initialize stats variables on CPU because eigen decomp is + # computed on CPU + with tf.device('/cpu'): + tmpStatsCache = {} + + # search for tensor factors and + # use block diag approx for the bias units + for var in varlist: + fpropFactor = factors[var]['fpropFactors_concat'] + bpropFactor = factors[var]['bpropFactors_concat'] + opType = factors[var]['opName'] + if opType == 'Conv2D': + Kh = var.get_shape()[0] + Kw = var.get_shape()[1] + C = fpropFactor.get_shape()[-1] + + Oh = bpropFactor.get_shape()[1] + Ow = bpropFactor.get_shape()[2] + if Oh == 1 and Ow == 1 and self._channel_fac: + # factorization along the channels do not support + # homogeneous coordinate + var_assnBias = factors[var]['assnBias'] + if var_assnBias: + factors[var]['assnBias'] = None + factors[var_assnBias]['assnWeights'] = None + ## + + for var in varlist: + fpropFactor = factors[var]['fpropFactors_concat'] + bpropFactor = factors[var]['bpropFactors_concat'] + opType = factors[var]['opName'] + self.stats[var] = {'opName': opType, + 'fprop_concat_stats': [], + 'bprop_concat_stats': [], + 'assnWeights': factors[var]['assnWeights'], + 'assnBias': factors[var]['assnBias'], + } + if fpropFactor is not None: + if fpropFactor not in tmpStatsCache: + if opType == 'Conv2D': + Kh = var.get_shape()[0] + Kw = var.get_shape()[1] + C = fpropFactor.get_shape()[-1] + + Oh = bpropFactor.get_shape()[1] + Ow = bpropFactor.get_shape()[2] + if Oh == 1 and Ow == 1 and self._channel_fac: + # factorization along the channels + # assume independence between input channels and spatial + # 2K-1 x 2K-1 covariance matrix and C x C covariance matrix + # factorization along the channels do not + # support homogeneous coordinate, assnBias + # is always None + fpropFactor2_size = Kh * Kw + slot_fpropFactor_stats2 = tf.Variable(tf.linalg.tensor_diag(tf.ones( + [fpropFactor2_size])) * self._diag_init_coeff, name='KFAC_STATS/' + fpropFactor.op.name, trainable=False) + self.stats[var]['fprop_concat_stats'].append( + slot_fpropFactor_stats2) + + fpropFactor_size = C + else: + # 2K-1 x 2K-1 x C x C covariance matrix + # assume BHWC + fpropFactor_size = Kh * Kw * C + else: + # D x D covariance matrix + fpropFactor_size = fpropFactor.get_shape()[-1] + + # use homogeneous coordinate + if not self._blockdiag_bias and self.stats[var]['assnBias']: + fpropFactor_size += 1 + + slot_fpropFactor_stats = tf.Variable(tf.linalg.tensor_diag(tf.ones( + [fpropFactor_size])) * self._diag_init_coeff, name='KFAC_STATS/' + fpropFactor.op.name, trainable=False) + self.stats[var]['fprop_concat_stats'].append( + slot_fpropFactor_stats) + if opType != 'Conv2D': + tmpStatsCache[fpropFactor] = self.stats[ + var]['fprop_concat_stats'] + else: + self.stats[var][ + 'fprop_concat_stats'] = tmpStatsCache[fpropFactor] + + if bpropFactor is not None: + # no need to collect backward stats for bias vectors if + # using homogeneous coordinates + if not((not self._blockdiag_bias) and self.stats[var]['assnWeights']): + if bpropFactor not in tmpStatsCache: + slot_bpropFactor_stats = tf.Variable(tf.linalg.tensor_diag(tf.ones([bpropFactor.get_shape( + )[-1]])) * self._diag_init_coeff, name='KFAC_STATS/' + bpropFactor.op.name, trainable=False) + self.stats[var]['bprop_concat_stats'].append( + slot_bpropFactor_stats) + tmpStatsCache[bpropFactor] = self.stats[ + var]['bprop_concat_stats'] + else: + self.stats[var][ + 'bprop_concat_stats'] = tmpStatsCache[bpropFactor] + + return self.stats + + def compute_and_apply_stats(self, loss_sampled, var_list=None): + varlist = var_list + if varlist is None: + varlist = tf.compat.v1.trainable_variables() + + stats = self.compute_stats(loss_sampled, var_list=varlist) + return self.apply_stats(stats) + + def compute_stats(self, loss_sampled, var_list=None): + varlist = var_list + if varlist is None: + varlist = tf.compat.v1.trainable_variables() + + gs = tf.gradients(ys=loss_sampled, xs=varlist, name='gradientsSampled') + self.gs = gs + factors = self.getFactors(gs, varlist) + stats = self.getStats(factors, varlist) + + updateOps = [] + statsUpdates = {} + statsUpdates_cache = {} + for var in varlist: + opType = factors[var]['opName'] + fops = factors[var]['op'] + fpropFactor = factors[var]['fpropFactors_concat'] + fpropStats_vars = stats[var]['fprop_concat_stats'] + bpropFactor = factors[var]['bpropFactors_concat'] + bpropStats_vars = stats[var]['bprop_concat_stats'] + SVD_factors = {} + for stats_var in fpropStats_vars: + stats_var_dim = int(stats_var.get_shape()[0]) + if stats_var not in statsUpdates_cache: + old_fpropFactor = fpropFactor + B = (tf.shape(input=fpropFactor)[0]) # batch size + if opType == 'Conv2D': + strides = fops.get_attr("strides") + padding = fops.get_attr("padding") + convkernel_size = var.get_shape()[0:3] + + KH = int(convkernel_size[0]) + KW = int(convkernel_size[1]) + C = int(convkernel_size[2]) + flatten_size = int(KH * KW * C) + + Oh = int(bpropFactor.get_shape()[1]) + Ow = int(bpropFactor.get_shape()[2]) + + if Oh == 1 and Ow == 1 and self._channel_fac: + # factorization along the channels + # assume independence among input channels + # factor = B x 1 x 1 x (KH xKW x C) + # patches = B x Oh x Ow x (KH xKW x C) + if len(SVD_factors) == 0: + if KFAC_DEBUG: + print(('approx %s act factor with rank-1 SVD factors' % (var.name))) + # find closest rank-1 approx to the feature map + S, U, V = tf.batch_svd(tf.reshape( + fpropFactor, [-1, KH * KW, C])) + # get rank-1 approx slides + sqrtS1 = tf.expand_dims(tf.sqrt(S[:, 0, 0]), 1) + patches_k = U[:, :, 0] * sqrtS1 # B x KH*KW + full_factor_shape = fpropFactor.get_shape() + patches_k.set_shape( + [full_factor_shape[0], KH * KW]) + patches_c = V[:, :, 0] * sqrtS1 # B x C + patches_c.set_shape([full_factor_shape[0], C]) + SVD_factors[C] = patches_c + SVD_factors[KH * KW] = patches_k + fpropFactor = SVD_factors[stats_var_dim] + + else: + # poor mem usage implementation + patches = tf.image.extract_patches(fpropFactor, sizes=[1, convkernel_size[ + 0], convkernel_size[1], 1], strides=strides, rates=[1, 1, 1, 1], padding=padding) + + if self._approxT2: + if KFAC_DEBUG: + print(('approxT2 act fisher for %s' % (var.name))) + # T^2 terms * 1/T^2, size: B x C + fpropFactor = tf.reduce_mean(input_tensor=patches, axis=[1, 2]) + else: + # size: (B x Oh x Ow) x C + fpropFactor = tf.reshape( + patches, [-1, flatten_size]) / Oh / Ow + fpropFactor_size = int(fpropFactor.get_shape()[-1]) + if stats_var_dim == (fpropFactor_size + 1) and not self._blockdiag_bias: + if opType == 'Conv2D' and not self._approxT2: + # correct padding for numerical stability (we + # divided out OhxOw from activations for T1 approx) + fpropFactor = tf.concat([fpropFactor, tf.ones( + [tf.shape(input=fpropFactor)[0], 1]) / Oh / Ow], 1) + else: + # use homogeneous coordinates + fpropFactor = tf.concat( + [fpropFactor, tf.ones([tf.shape(input=fpropFactor)[0], 1])], 1) + + # average over the number of data points in a batch + # divided by B + cov = tf.matmul(fpropFactor, fpropFactor, + transpose_a=True) / tf.cast(B, tf.float32) + updateOps.append(cov) + statsUpdates[stats_var] = cov + if opType != 'Conv2D': + # HACK: for convolution we recompute fprop stats for + # every layer including forking layers + statsUpdates_cache[stats_var] = cov + + for stats_var in bpropStats_vars: + stats_var_dim = int(stats_var.get_shape()[0]) + if stats_var not in statsUpdates_cache: + old_bpropFactor = bpropFactor + bpropFactor_shape = bpropFactor.get_shape() + B = tf.shape(input=bpropFactor)[0] # batch size + C = int(bpropFactor_shape[-1]) # num channels + if opType == 'Conv2D' or len(bpropFactor_shape) == 4: + if fpropFactor is not None: + if self._approxT2: + if KFAC_DEBUG: + print(('approxT2 grad fisher for %s' % (var.name))) + bpropFactor = tf.reduce_sum( + input_tensor=bpropFactor, axis=[1, 2]) # T^2 terms * 1/T^2 + else: + bpropFactor = tf.reshape( + bpropFactor, [-1, C]) * Oh * Ow # T * 1/T terms + else: + # just doing block diag approx. spatial independent + # structure does not apply here. summing over + # spatial locations + if KFAC_DEBUG: + print(('block diag approx fisher for %s' % (var.name))) + bpropFactor = tf.reduce_sum(input_tensor=bpropFactor, axis=[1, 2]) + + # assume sampled loss is averaged. TO-DO:figure out better + # way to handle this + bpropFactor *= tf.cast(B, dtype=tf.float32) + ## + + cov_b = tf.matmul( + bpropFactor, bpropFactor, transpose_a=True) / tf.cast(tf.shape(input=bpropFactor)[0], dtype=tf.float32) + + updateOps.append(cov_b) + statsUpdates[stats_var] = cov_b + statsUpdates_cache[stats_var] = cov_b + + if KFAC_DEBUG: + aKey = list(statsUpdates.keys())[0] + statsUpdates[aKey] = tf.compat.v1.Print(statsUpdates[aKey], + [tf.convert_to_tensor(value='step:'), + self.global_step, + tf.convert_to_tensor( + value='computing stats'), + ]) + self.statsUpdates = statsUpdates + return statsUpdates + + def apply_stats(self, statsUpdates): + """ compute stats and update/apply the new stats to the running average + """ + + def updateAccumStats(): + if self._full_stats_init: + return tf.cond(pred=tf.greater(self.sgd_step, self._cold_iter), true_fn=lambda: tf.group(*self._apply_stats(statsUpdates, accumulate=True, accumulateCoeff=1. / self._stats_accum_iter)), false_fn=tf.no_op) + else: + return tf.group(*self._apply_stats(statsUpdates, accumulate=True, accumulateCoeff=1. / self._stats_accum_iter)) + + def updateRunningAvgStats(statsUpdates, fac_iter=1): + # return tf.cond(tf.greater_equal(self.factor_step, + # tf.convert_to_tensor(fac_iter)), lambda: + # tf.group(*self._apply_stats(stats_list, varlist)), tf.no_op) + return tf.group(*self._apply_stats(statsUpdates)) + + if self._async_stats: + # asynchronous stats update + update_stats = self._apply_stats(statsUpdates) + + queue = tf.queue.FIFOQueue(1, [item.dtype for item in update_stats], shapes=[ + item.get_shape() for item in update_stats]) + enqueue_op = queue.enqueue(update_stats) + + def dequeue_stats_op(): + return queue.dequeue() + self.qr_stats = tf.compat.v1.train.QueueRunner(queue, [enqueue_op]) + update_stats_op = tf.cond(pred=tf.equal(queue.size(), tf.convert_to_tensor( + value=0)), true_fn=tf.no_op, false_fn=lambda: tf.group(*[dequeue_stats_op(), ])) + else: + # synchronous stats update + update_stats_op = tf.cond(pred=tf.greater_equal( + self.stats_step, self._stats_accum_iter), true_fn=lambda: updateRunningAvgStats(statsUpdates), false_fn=updateAccumStats) + self._update_stats_op = update_stats_op + return update_stats_op + + def _apply_stats(self, statsUpdates, accumulate=False, accumulateCoeff=0.): + updateOps = [] + # obtain the stats var list + for stats_var in statsUpdates: + stats_new = statsUpdates[stats_var] + if accumulate: + # simple superbatch averaging + update_op = tf.compat.v1.assign_add( + stats_var, accumulateCoeff * stats_new, use_locking=True) + else: + # exponential running averaging + update_op = tf.compat.v1.assign( + stats_var, stats_var * self._stats_decay, use_locking=True) + update_op = tf.compat.v1.assign_add( + update_op, (1. - self._stats_decay) * stats_new, use_locking=True) + updateOps.append(update_op) + + with tf.control_dependencies(updateOps): + stats_step_op = tf.compat.v1.assign_add(self.stats_step, 1) + + if KFAC_DEBUG: + stats_step_op = (tf.compat.v1.Print(stats_step_op, + [tf.convert_to_tensor(value='step:'), + self.global_step, + tf.convert_to_tensor(value='fac step:'), + self.factor_step, + tf.convert_to_tensor(value='sgd step:'), + self.sgd_step, + tf.convert_to_tensor(value='Accum:'), + tf.convert_to_tensor(value=accumulate), + tf.convert_to_tensor(value='Accum coeff:'), + tf.convert_to_tensor(value=accumulateCoeff), + tf.convert_to_tensor(value='stat step:'), + self.stats_step, updateOps[0], updateOps[1]])) + return [stats_step_op, ] + + def getStatsEigen(self, stats=None): + if len(self.stats_eigen) == 0: + stats_eigen = {} + if stats is None: + stats = self.stats + + tmpEigenCache = {} + with tf.device('/cpu:0'): + for var in stats: + for key in ['fprop_concat_stats', 'bprop_concat_stats']: + for stats_var in stats[var][key]: + if stats_var not in tmpEigenCache: + stats_dim = stats_var.get_shape()[1].value + e = tf.Variable(tf.ones( + [stats_dim]), name='KFAC_FAC/' + stats_var.name.split(':')[0] + '/e', trainable=False) + Q = tf.Variable(tf.linalg.tensor_diag(tf.ones( + [stats_dim])), name='KFAC_FAC/' + stats_var.name.split(':')[0] + '/Q', trainable=False) + stats_eigen[stats_var] = {'e': e, 'Q': Q} + tmpEigenCache[ + stats_var] = stats_eigen[stats_var] + else: + stats_eigen[stats_var] = tmpEigenCache[ + stats_var] + self.stats_eigen = stats_eigen + return self.stats_eigen + + def computeStatsEigen(self): + """ compute the eigen decomp using copied var stats to avoid concurrent read/write from other queue """ + # TO-DO: figure out why this op has delays (possibly moving + # eigenvectors around?) + with tf.device('/cpu:0'): + def removeNone(tensor_list): + local_list = [] + for item in tensor_list: + if item is not None: + local_list.append(item) + return local_list + + def copyStats(var_list): + print("copying stats to buffer tensors before eigen decomp") + redundant_stats = {} + copied_list = [] + for item in var_list: + if item is not None: + if item not in redundant_stats: + if self._use_float64: + redundant_stats[item] = tf.cast( + tf.identity(item), tf.float64) + else: + redundant_stats[item] = tf.identity(item) + copied_list.append(redundant_stats[item]) + else: + copied_list.append(None) + return copied_list + #stats = [copyStats(self.fStats), copyStats(self.bStats)] + #stats = [self.fStats, self.bStats] + + stats_eigen = self.stats_eigen + computedEigen = {} + eigen_reverse_lookup = {} + updateOps = [] + # sync copied stats + # with tf.control_dependencies(removeNone(stats[0]) + + # removeNone(stats[1])): + with tf.control_dependencies([]): + for stats_var in stats_eigen: + if stats_var not in computedEigen: + eigens = tf.linalg.eigh(stats_var) + e = eigens[0] + Q = eigens[1] + if self._use_float64: + e = tf.cast(e, tf.float32) + Q = tf.cast(Q, tf.float32) + updateOps.append(e) + updateOps.append(Q) + computedEigen[stats_var] = {'e': e, 'Q': Q} + eigen_reverse_lookup[e] = stats_eigen[stats_var]['e'] + eigen_reverse_lookup[Q] = stats_eigen[stats_var]['Q'] + + self.eigen_reverse_lookup = eigen_reverse_lookup + self.eigen_update_list = updateOps + + if KFAC_DEBUG: + self.eigen_update_list = [item for item in updateOps] + with tf.control_dependencies(updateOps): + updateOps.append(tf.compat.v1.Print(tf.constant( + 0.), [tf.convert_to_tensor(value='computed factor eigen')])) + + return updateOps + + def applyStatsEigen(self, eigen_list): + updateOps = [] + print(('updating %d eigenvalue/vectors' % len(eigen_list))) + for i, (tensor, mark) in enumerate(zip(eigen_list, self.eigen_update_list)): + stats_eigen_var = self.eigen_reverse_lookup[mark] + updateOps.append( + tf.compat.v1.assign(stats_eigen_var, tensor, use_locking=True)) + + with tf.control_dependencies(updateOps): + factor_step_op = tf.compat.v1.assign_add(self.factor_step, 1) + updateOps.append(factor_step_op) + if KFAC_DEBUG: + updateOps.append(tf.compat.v1.Print(tf.constant( + 0.), [tf.convert_to_tensor(value='updated kfac factors')])) + return updateOps + + def getKfacPrecondUpdates(self, gradlist, varlist): + updatelist = [] + vg = 0. + + assert len(self.stats) > 0 + assert len(self.stats_eigen) > 0 + assert len(self.factors) > 0 + counter = 0 + + grad_dict = {var: grad for grad, var in zip(gradlist, varlist)} + + for grad, var in zip(gradlist, varlist): + GRAD_RESHAPE = False + GRAD_TRANSPOSE = False + + fpropFactoredFishers = self.stats[var]['fprop_concat_stats'] + bpropFactoredFishers = self.stats[var]['bprop_concat_stats'] + + if (len(fpropFactoredFishers) + len(bpropFactoredFishers)) > 0: + counter += 1 + GRAD_SHAPE = grad.get_shape() + if len(grad.get_shape()) > 2: + # reshape conv kernel parameters + KW = int(grad.get_shape()[0]) + KH = int(grad.get_shape()[1]) + C = int(grad.get_shape()[2]) + D = int(grad.get_shape()[3]) + + if len(fpropFactoredFishers) > 1 and self._channel_fac: + # reshape conv kernel parameters into tensor + grad = tf.reshape(grad, [KW * KH, C, D]) + else: + # reshape conv kernel parameters into 2D grad + grad = tf.reshape(grad, [-1, D]) + GRAD_RESHAPE = True + elif len(grad.get_shape()) == 1: + # reshape bias or 1D parameters + D = int(grad.get_shape()[0]) + + grad = tf.expand_dims(grad, 0) + GRAD_RESHAPE = True + else: + # 2D parameters + C = int(grad.get_shape()[0]) + D = int(grad.get_shape()[1]) + + if (self.stats[var]['assnBias'] is not None) and not self._blockdiag_bias: + # use homogeneous coordinates only works for 2D grad. + # TO-DO: figure out how to factorize bias grad + # stack bias grad + var_assnBias = self.stats[var]['assnBias'] + grad = tf.concat( + [grad, tf.expand_dims(grad_dict[var_assnBias], 0)], 0) + + # project gradient to eigen space and reshape the eigenvalues + # for broadcasting + eigVals = [] + + for idx, stats in enumerate(self.stats[var]['fprop_concat_stats']): + Q = self.stats_eigen[stats]['Q'] + e = detectMinVal(self.stats_eigen[stats][ + 'e'], var, name='act', debug=KFAC_DEBUG) + + Q, e = factorReshape(Q, e, grad, facIndx=idx, ftype='act') + eigVals.append(e) + grad = gmatmul(Q, grad, transpose_a=True, reduce_dim=idx) + + for idx, stats in enumerate(self.stats[var]['bprop_concat_stats']): + Q = self.stats_eigen[stats]['Q'] + e = detectMinVal(self.stats_eigen[stats][ + 'e'], var, name='grad', debug=KFAC_DEBUG) + + Q, e = factorReshape(Q, e, grad, facIndx=idx, ftype='grad') + eigVals.append(e) + grad = gmatmul(grad, Q, transpose_b=False, reduce_dim=idx) + ## + + ##### + # whiten using eigenvalues + weightDecayCoeff = 0. + if var in self._weight_decay_dict: + weightDecayCoeff = self._weight_decay_dict[var] + if KFAC_DEBUG: + print(('weight decay coeff for %s is %f' % (var.name, weightDecayCoeff))) + + if self._factored_damping: + if KFAC_DEBUG: + print(('use factored damping for %s' % (var.name))) + coeffs = 1. + num_factors = len(eigVals) + # compute the ratio of two trace norm of the left and right + # KFac matrices, and their generalization + if len(eigVals) == 1: + damping = self._epsilon + weightDecayCoeff + else: + damping = tf.pow( + self._epsilon + weightDecayCoeff, 1. / num_factors) + eigVals_tnorm_avg = [tf.reduce_mean( + input_tensor=tf.abs(e)) for e in eigVals] + for e, e_tnorm in zip(eigVals, eigVals_tnorm_avg): + eig_tnorm_negList = [ + item for item in eigVals_tnorm_avg if item != e_tnorm] + if len(eigVals) == 1: + adjustment = 1. + elif len(eigVals) == 2: + adjustment = tf.sqrt( + e_tnorm / eig_tnorm_negList[0]) + else: + eig_tnorm_negList_prod = reduce( + lambda x, y: x * y, eig_tnorm_negList) + adjustment = tf.pow( + tf.pow(e_tnorm, num_factors - 1.) / eig_tnorm_negList_prod, 1. / num_factors) + coeffs *= (e + adjustment * damping) + else: + coeffs = 1. + damping = (self._epsilon + weightDecayCoeff) + for e in eigVals: + coeffs *= e + coeffs += damping + + #grad = tf.Print(grad, [tf.convert_to_tensor('1'), tf.convert_to_tensor(var.name), grad.get_shape()]) + + grad /= coeffs + + #grad = tf.Print(grad, [tf.convert_to_tensor('2'), tf.convert_to_tensor(var.name), grad.get_shape()]) + ##### + # project gradient back to euclidean space + for idx, stats in enumerate(self.stats[var]['fprop_concat_stats']): + Q = self.stats_eigen[stats]['Q'] + grad = gmatmul(Q, grad, transpose_a=False, reduce_dim=idx) + + for idx, stats in enumerate(self.stats[var]['bprop_concat_stats']): + Q = self.stats_eigen[stats]['Q'] + grad = gmatmul(grad, Q, transpose_b=True, reduce_dim=idx) + ## + + #grad = tf.Print(grad, [tf.convert_to_tensor('3'), tf.convert_to_tensor(var.name), grad.get_shape()]) + if (self.stats[var]['assnBias'] is not None) and not self._blockdiag_bias: + # use homogeneous coordinates only works for 2D grad. + # TO-DO: figure out how to factorize bias grad + # un-stack bias grad + var_assnBias = self.stats[var]['assnBias'] + C_plus_one = int(grad.get_shape()[0]) + grad_assnBias = tf.reshape(tf.slice(grad, + begin=[ + C_plus_one - 1, 0], + size=[1, -1]), var_assnBias.get_shape()) + grad_assnWeights = tf.slice(grad, + begin=[0, 0], + size=[C_plus_one - 1, -1]) + grad_dict[var_assnBias] = grad_assnBias + grad = grad_assnWeights + + #grad = tf.Print(grad, [tf.convert_to_tensor('4'), tf.convert_to_tensor(var.name), grad.get_shape()]) + if GRAD_RESHAPE: + grad = tf.reshape(grad, GRAD_SHAPE) + + grad_dict[var] = grad + + print(('projecting %d gradient matrices' % counter)) + + for g, var in zip(gradlist, varlist): + grad = grad_dict[var] + ### clipping ### + if KFAC_DEBUG: + print(('apply clipping to %s' % (var.name))) + tf.compat.v1.Print(grad, [tf.sqrt(tf.reduce_sum(input_tensor=tf.pow(grad, 2)))], "Euclidean norm of new grad") + local_vg = tf.reduce_sum(input_tensor=grad * g * (self._lr * self._lr)) + vg += local_vg + + # recale everything + if KFAC_DEBUG: + print('apply vFv clipping') + + scaling = tf.minimum(1., tf.sqrt(self._clip_kl / vg)) + if KFAC_DEBUG: + scaling = tf.compat.v1.Print(scaling, [tf.convert_to_tensor( + value='clip: '), scaling, tf.convert_to_tensor(value=' vFv: '), vg]) + with tf.control_dependencies([tf.compat.v1.assign(self.vFv, vg)]): + updatelist = [grad_dict[var] for var in varlist] + for i, item in enumerate(updatelist): + updatelist[i] = scaling * item + + return updatelist + + def compute_gradients(self, loss, var_list=None): + varlist = var_list + if varlist is None: + varlist = tf.compat.v1.trainable_variables() + g = tf.gradients(ys=loss, xs=varlist) + + return [(a, b) for a, b in zip(g, varlist)] + + def apply_gradients_kfac(self, grads): + g, varlist = list(zip(*grads)) + + if len(self.stats_eigen) == 0: + self.getStatsEigen() + + qr = None + # launch eigen-decomp on a queue thread + if self._async: + print('Use async eigen decomp') + # get a list of factor loading tensors + factorOps_dummy = self.computeStatsEigen() + + # define a queue for the list of factor loading tensors + queue = tf.queue.FIFOQueue(1, [item.dtype for item in factorOps_dummy], shapes=[ + item.get_shape() for item in factorOps_dummy]) + enqueue_op = tf.cond(pred=tf.logical_and(tf.equal(tf.math.floormod(self.stats_step, self._kfac_update), tf.convert_to_tensor( + value=0)), tf.greater_equal(self.stats_step, self._stats_accum_iter)), true_fn=lambda: queue.enqueue(self.computeStatsEigen()), false_fn=tf.no_op) + + def dequeue_op(): + return queue.dequeue() + + qr = tf.compat.v1.train.QueueRunner(queue, [enqueue_op]) + + updateOps = [] + global_step_op = tf.compat.v1.assign_add(self.global_step, 1) + updateOps.append(global_step_op) + + with tf.control_dependencies([global_step_op]): + + # compute updates + assert self._update_stats_op != None + updateOps.append(self._update_stats_op) + dependency_list = [] + if not self._async: + dependency_list.append(self._update_stats_op) + + with tf.control_dependencies(dependency_list): + def no_op_wrapper(): + return tf.group(*[tf.compat.v1.assign_add(self.cold_step, 1)]) + + if not self._async: + # synchronous eigen-decomp updates + updateFactorOps = tf.cond(pred=tf.logical_and(tf.equal(tf.math.floormod(self.stats_step, self._kfac_update), + tf.convert_to_tensor(value=0)), + tf.greater_equal(self.stats_step, self._stats_accum_iter)), true_fn=lambda: tf.group(*self.applyStatsEigen(self.computeStatsEigen())), false_fn=no_op_wrapper) + else: + # asynchronous eigen-decomp updates using queue + updateFactorOps = tf.cond(pred=tf.greater_equal(self.stats_step, self._stats_accum_iter), + true_fn=lambda: tf.cond(pred=tf.equal(queue.size(), tf.convert_to_tensor(value=0)), + true_fn=tf.no_op, + + false_fn=lambda: tf.group( + *self.applyStatsEigen(dequeue_op())), + ), + false_fn=no_op_wrapper) + + updateOps.append(updateFactorOps) + + with tf.control_dependencies([updateFactorOps]): + def gradOp(): + return list(g) + + def getKfacGradOp(): + return self.getKfacPrecondUpdates(g, varlist) + u = tf.cond(pred=tf.greater(self.factor_step, + tf.convert_to_tensor(value=0)), true_fn=getKfacGradOp, false_fn=gradOp) + + optim = tf.compat.v1.train.MomentumOptimizer( + self._lr * (1. - self._momentum), self._momentum) + #optim = tf.train.AdamOptimizer(self._lr, epsilon=0.01) + + def optimOp(): + def updateOptimOp(): + if self._full_stats_init: + return tf.cond(pred=tf.greater(self.factor_step, tf.convert_to_tensor(value=0)), true_fn=lambda: optim.apply_gradients(list(zip(u, varlist))), false_fn=tf.no_op) + else: + return optim.apply_gradients(list(zip(u, varlist))) + if self._full_stats_init: + return tf.cond(pred=tf.greater_equal(self.stats_step, self._stats_accum_iter), true_fn=updateOptimOp, false_fn=tf.no_op) + else: + return tf.cond(pred=tf.greater_equal(self.sgd_step, self._cold_iter), true_fn=updateOptimOp, false_fn=tf.no_op) + updateOps.append(optimOp()) + + return tf.group(*updateOps), qr + + def apply_gradients(self, grads): + coldOptim = tf.compat.v1.train.MomentumOptimizer( + self._cold_lr, self._momentum) + + def coldSGDstart(): + sgd_grads, sgd_var = zip(*grads) + + if self.max_grad_norm != None: + sgd_grads, sgd_grad_norm = tf.clip_by_global_norm(sgd_grads,self.max_grad_norm) + + sgd_grads = list(zip(sgd_grads,sgd_var)) + + sgd_step_op = tf.compat.v1.assign_add(self.sgd_step, 1) + coldOptim_op = coldOptim.apply_gradients(sgd_grads) + if KFAC_DEBUG: + with tf.control_dependencies([sgd_step_op, coldOptim_op]): + sgd_step_op = tf.compat.v1.Print( + sgd_step_op, [self.sgd_step, tf.convert_to_tensor(value='doing cold sgd step')]) + return tf.group(*[sgd_step_op, coldOptim_op]) + + kfacOptim_op, qr = self.apply_gradients_kfac(grads) + + def warmKFACstart(): + return kfacOptim_op + + return tf.cond(pred=tf.greater(self.sgd_step, self._cold_iter), true_fn=warmKFACstart, false_fn=coldSGDstart), qr + + def minimize(self, loss, loss_sampled, var_list=None): + grads = self.compute_gradients(loss, var_list=var_list) + update_stats_op = self.compute_and_apply_stats( + loss_sampled, var_list=var_list) + return self.apply_gradients(grads) diff --git a/baselines/acktr/kfac_utils.py b/baselines/acktr/kfac_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a34afe1ae3a08e3c204633333c336ae3d4226d7c --- /dev/null +++ b/baselines/acktr/kfac_utils.py @@ -0,0 +1,86 @@ +import tensorflow as tf + +def gmatmul(a, b, transpose_a=False, transpose_b=False, reduce_dim=None): + assert reduce_dim is not None + + # weird batch matmul + if len(a.get_shape()) == 2 and len(b.get_shape()) > 2: + # reshape reduce_dim to the left most dim in b + b_shape = b.get_shape() + if reduce_dim != 0: + b_dims = list(range(len(b_shape))) + b_dims.remove(reduce_dim) + b_dims.insert(0, reduce_dim) + b = tf.transpose(a=b, perm=b_dims) + b_t_shape = b.get_shape() + b = tf.reshape(b, [int(b_shape[reduce_dim]), -1]) + result = tf.matmul(a, b, transpose_a=transpose_a, + transpose_b=transpose_b) + result = tf.reshape(result, b_t_shape) + if reduce_dim != 0: + b_dims = list(range(len(b_shape))) + b_dims.remove(0) + b_dims.insert(reduce_dim, 0) + result = tf.transpose(a=result, perm=b_dims) + return result + + elif len(a.get_shape()) > 2 and len(b.get_shape()) == 2: + # reshape reduce_dim to the right most dim in a + a_shape = a.get_shape() + outter_dim = len(a_shape) - 1 + reduce_dim = len(a_shape) - reduce_dim - 1 + if reduce_dim != outter_dim: + a_dims = list(range(len(a_shape))) + a_dims.remove(reduce_dim) + a_dims.insert(outter_dim, reduce_dim) + a = tf.transpose(a=a, perm=a_dims) + a_t_shape = a.get_shape() + a = tf.reshape(a, [-1, int(a_shape[reduce_dim])]) + result = tf.matmul(a, b, transpose_a=transpose_a, + transpose_b=transpose_b) + result = tf.reshape(result, a_t_shape) + if reduce_dim != outter_dim: + a_dims = list(range(len(a_shape))) + a_dims.remove(outter_dim) + a_dims.insert(reduce_dim, outter_dim) + result = tf.transpose(a=result, perm=a_dims) + return result + + elif len(a.get_shape()) == 2 and len(b.get_shape()) == 2: + return tf.matmul(a, b, transpose_a=transpose_a, transpose_b=transpose_b) + + assert False, 'something went wrong' + + +def clipoutNeg(vec, threshold=1e-6): + mask = tf.cast(vec > threshold, tf.float32) + return mask * vec + + +def detectMinVal(input_mat, var, threshold=1e-6, name='', debug=False): + eigen_min = tf.reduce_min(input_tensor=input_mat) + eigen_max = tf.reduce_max(input_tensor=input_mat) + eigen_ratio = eigen_max / eigen_min + input_mat_clipped = clipoutNeg(input_mat, threshold) + + if debug: + input_mat_clipped = tf.cond(pred=tf.logical_or(tf.greater(eigen_ratio, 0.), tf.less(eigen_ratio, -500)), true_fn=lambda: input_mat_clipped, false_fn=lambda: tf.compat.v1.Print( + input_mat_clipped, [tf.convert_to_tensor(value='screwed ratio ' + name + ' eigen values!!!'), tf.convert_to_tensor(value=var.name), eigen_min, eigen_max, eigen_ratio])) + + return input_mat_clipped + + +def factorReshape(Q, e, grad, facIndx=0, ftype='act'): + grad_shape = grad.get_shape() + if ftype == 'act': + assert e.get_shape()[0] == grad_shape[facIndx] + expanded_shape = [1, ] * len(grad_shape) + expanded_shape[facIndx] = -1 + e = tf.reshape(e, expanded_shape) + if ftype == 'grad': + assert e.get_shape()[0] == grad_shape[len(grad_shape) - facIndx - 1] + expanded_shape = [1, ] * len(grad_shape) + expanded_shape[len(grad_shape) - facIndx - 1] = -1 + e = tf.reshape(e, expanded_shape) + + return Q, e diff --git a/baselines/acktr/utils.py b/baselines/acktr/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a9217c292069fcd50b4b9829738316fa6fa86028 --- /dev/null +++ b/baselines/acktr/utils.py @@ -0,0 +1,28 @@ +import tensorflow as tf + +def dense(x, size, name, weight_init=None, bias_init=0, weight_loss_dict=None, reuse=None): + with tf.compat.v1.variable_scope(name, reuse=reuse): + assert (len(tf.compat.v1.get_variable_scope().name.split('/')) == 2) + + w = tf.compat.v1.get_variable("w", [x.get_shape()[1], size], initializer=weight_init) + b = tf.compat.v1.get_variable("b", [size], initializer=tf.compat.v1.constant_initializer(bias_init)) + weight_decay_fc = 3e-4 + + if weight_loss_dict is not None: + weight_decay = tf.multiply(tf.nn.l2_loss(w), weight_decay_fc, name='weight_decay_loss') + if weight_loss_dict is not None: + weight_loss_dict[w] = weight_decay_fc + weight_loss_dict[b] = 0.0 + + tf.compat.v1.add_to_collection(tf.compat.v1.get_variable_scope().name.split('/')[0] + '_' + 'losses', weight_decay) + + return tf.nn.bias_add(tf.matmul(x, w), b) + +def kl_div(action_dist1, action_dist2, action_size): + mean1, std1 = action_dist1[:, :action_size], action_dist1[:, action_size:] + mean2, std2 = action_dist2[:, :action_size], action_dist2[:, action_size:] + + numerator = tf.square(mean1 - mean2) + tf.square(std1) - tf.square(std2) + denominator = 2 * tf.square(std2) + 1e-8 + return tf.reduce_sum( + input_tensor=numerator/denominator + tf.math.log(std2) - tf.math.log(std1),axis=-1) diff --git a/baselines/bench/__init__.py b/baselines/bench/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fc2e05b74e22a2ab2c5892ab2374d9aad5fdfc42 --- /dev/null +++ b/baselines/bench/__init__.py @@ -0,0 +1,3 @@ +# flake8: noqa F403 +from baselines.bench.benchmarks import * +from baselines.bench.monitor import * diff --git a/baselines/bench/benchmarks.py b/baselines/bench/benchmarks.py new file mode 100644 index 0000000000000000000000000000000000000000..5d626ded231572abc257a39db32f55e67aa9b6c9 --- /dev/null +++ b/baselines/bench/benchmarks.py @@ -0,0 +1,164 @@ +import re +import os +SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) + +_atari7 = ['BeamRider', 'Breakout', 'Enduro', 'Pong', 'Qbert', 'Seaquest', 'SpaceInvaders'] +_atariexpl7 = ['Freeway', 'Gravitar', 'MontezumaRevenge', 'Pitfall', 'PrivateEye', 'Solaris', 'Venture'] + +_BENCHMARKS = [] + +remove_version_re = re.compile(r'-v\d+$') + + +def register_benchmark(benchmark): + for b in _BENCHMARKS: + if b['name'] == benchmark['name']: + raise ValueError('Benchmark with name %s already registered!' % b['name']) + + # automatically add a description if it is not present + if 'tasks' in benchmark: + for t in benchmark['tasks']: + if 'desc' not in t: + t['desc'] = remove_version_re.sub('', t.get('env_id', t.get('id'))) + _BENCHMARKS.append(benchmark) + + +def list_benchmarks(): + return [b['name'] for b in _BENCHMARKS] + + +def get_benchmark(benchmark_name): + for b in _BENCHMARKS: + if b['name'] == benchmark_name: + return b + raise ValueError('%s not found! Known benchmarks: %s' % (benchmark_name, list_benchmarks())) + + +def get_task(benchmark, env_id): + """Get a task by env_id. Return None if the benchmark doesn't have the env""" + return next(filter(lambda task: task['env_id'] == env_id, benchmark['tasks']), None) + + +def find_task_for_env_id_in_any_benchmark(env_id): + for bm in _BENCHMARKS: + for task in bm["tasks"]: + if task["env_id"] == env_id: + return bm, task + return None, None + + +_ATARI_SUFFIX = 'NoFrameskip-v4' + +register_benchmark({ + 'name': 'Atari50M', + 'description': '7 Atari games from Mnih et al. (2013), with pixel observations, 50M timesteps', + 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 2, 'num_timesteps': int(50e6)} for _game in _atari7] +}) + +register_benchmark({ + 'name': 'Atari10M', + 'description': '7 Atari games from Mnih et al. (2013), with pixel observations, 10M timesteps', + 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 6, 'num_timesteps': int(10e6)} for _game in _atari7] +}) + +register_benchmark({ + 'name': 'Atari1Hr', + 'description': '7 Atari games from Mnih et al. (2013), with pixel observations, 1 hour of walltime', + 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 2, 'num_seconds': 60 * 60} for _game in _atari7] +}) + +register_benchmark({ + 'name': 'AtariExploration10M', + 'description': '7 Atari games emphasizing exploration, with pixel observations, 10M timesteps', + 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 2, 'num_timesteps': int(10e6)} for _game in _atariexpl7] +}) + + +# MuJoCo + +_mujocosmall = [ + 'InvertedDoublePendulum-v2', 'InvertedPendulum-v2', + 'HalfCheetah-v2', 'Hopper-v2', 'Walker2d-v2', + 'Reacher-v2', 'Swimmer-v2'] +register_benchmark({ + 'name': 'Mujoco1M', + 'description': 'Some small 2D MuJoCo tasks, run for 1M timesteps', + 'tasks': [{'env_id': _envid, 'trials': 6, 'num_timesteps': int(1e6)} for _envid in _mujocosmall] +}) + +register_benchmark({ + 'name': 'MujocoWalkers', + 'description': 'MuJoCo forward walkers, run for 8M, humanoid 100M', + 'tasks': [ + {'env_id': "Hopper-v1", 'trials': 4, 'num_timesteps': 8 * 1000000}, + {'env_id': "Walker2d-v1", 'trials': 4, 'num_timesteps': 8 * 1000000}, + {'env_id': "Humanoid-v1", 'trials': 4, 'num_timesteps': 100 * 1000000}, + ] +}) + +# Bullet +_bulletsmall = [ + 'InvertedDoublePendulum', 'InvertedPendulum', 'HalfCheetah', 'Reacher', 'Walker2D', 'Hopper', 'Ant' +] +_bulletsmall = [e + 'BulletEnv-v0' for e in _bulletsmall] + +register_benchmark({ + 'name': 'Bullet1M', + 'description': '6 mujoco-like tasks from bullet, 1M steps', + 'tasks': [{'env_id': e, 'trials': 6, 'num_timesteps': int(1e6)} for e in _bulletsmall] +}) + + +# Roboschool + +register_benchmark({ + 'name': 'Roboschool8M', + 'description': 'Small 2D tasks, up to 30 minutes to complete on 8 cores', + 'tasks': [ + {'env_id': "RoboschoolReacher-v1", 'trials': 4, 'num_timesteps': 2 * 1000000}, + {'env_id': "RoboschoolAnt-v1", 'trials': 4, 'num_timesteps': 8 * 1000000}, + {'env_id': "RoboschoolHalfCheetah-v1", 'trials': 4, 'num_timesteps': 8 * 1000000}, + {'env_id': "RoboschoolHopper-v1", 'trials': 4, 'num_timesteps': 8 * 1000000}, + {'env_id': "RoboschoolWalker2d-v1", 'trials': 4, 'num_timesteps': 8 * 1000000}, + ] +}) +register_benchmark({ + 'name': 'RoboschoolHarder', + 'description': 'Test your might!!! Up to 12 hours on 32 cores', + 'tasks': [ + {'env_id': "RoboschoolHumanoid-v1", 'trials': 4, 'num_timesteps': 100 * 1000000}, + {'env_id': "RoboschoolHumanoidFlagrun-v1", 'trials': 4, 'num_timesteps': 200 * 1000000}, + {'env_id': "RoboschoolHumanoidFlagrunHarder-v1", 'trials': 4, 'num_timesteps': 400 * 1000000}, + ] +}) + +# Other + +_atari50 = [ # actually 47 + 'Alien', 'Amidar', 'Assault', 'Asterix', 'Asteroids', + 'Atlantis', 'BankHeist', 'BattleZone', 'BeamRider', 'Bowling', + 'Breakout', 'Centipede', 'ChopperCommand', 'CrazyClimber', + 'DemonAttack', 'DoubleDunk', 'Enduro', 'FishingDerby', 'Freeway', + 'Frostbite', 'Gopher', 'Gravitar', 'IceHockey', 'Jamesbond', + 'Kangaroo', 'Krull', 'KungFuMaster', 'MontezumaRevenge', 'MsPacman', + 'NameThisGame', 'Pitfall', 'Pong', 'PrivateEye', 'Qbert', + 'RoadRunner', 'Robotank', 'Seaquest', 'SpaceInvaders', 'StarGunner', + 'Tennis', 'TimePilot', 'Tutankham', 'UpNDown', 'Venture', + 'VideoPinball', 'WizardOfWor', 'Zaxxon', +] + +register_benchmark({ + 'name': 'Atari50_10M', + 'description': '47 Atari games from Mnih et al. (2013), with pixel observations, 10M timesteps', + 'tasks': [{'desc': _game, 'env_id': _game + _ATARI_SUFFIX, 'trials': 2, 'num_timesteps': int(10e6)} for _game in _atari50] +}) + +# HER DDPG + +_fetch_tasks = ['FetchReach-v1', 'FetchPush-v1', 'FetchSlide-v1'] +register_benchmark({ + 'name': 'Fetch1M', + 'description': 'Fetch* benchmarks for 1M timesteps', + 'tasks': [{'trials': 6, 'env_id': env_id, 'num_timesteps': int(1e6)} for env_id in _fetch_tasks] +}) + diff --git a/baselines/bench/monitor.py b/baselines/bench/monitor.py new file mode 100644 index 0000000000000000000000000000000000000000..e63e71db5c0a1e7d421794e8a64b048a1a6cfc84 --- /dev/null +++ b/baselines/bench/monitor.py @@ -0,0 +1,162 @@ +__all__ = ['Monitor', 'get_monitor_files', 'load_results'] + +from gym.core import Wrapper +import time +from glob import glob +import csv +import os.path as osp +import json + +class Monitor(Wrapper): + EXT = "monitor.csv" + f = None + + def __init__(self, env, filename, allow_early_resets=False, reset_keywords=(), info_keywords=()): + Wrapper.__init__(self, env=env) + self.tstart = time.time() + if filename: + self.results_writer = ResultsWriter(filename, + header={"t_start": time.time(), 'env_id' : env.spec and env.spec.id}, + extra_keys=reset_keywords + info_keywords + ) + else: + self.results_writer = None + self.reset_keywords = reset_keywords + self.info_keywords = info_keywords + self.allow_early_resets = allow_early_resets + self.rewards = None + self.needs_reset = True + self.episode_rewards = [] + self.episode_lengths = [] + self.episode_times = [] + self.total_steps = 0 + self.current_reset_info = {} # extra info about the current episode, that was passed in during reset() + + def reset(self, **kwargs): + self.reset_state() + for k in self.reset_keywords: + v = kwargs.get(k) + if v is None: + raise ValueError('Expected you to pass kwarg %s into reset'%k) + self.current_reset_info[k] = v + return self.env.reset(**kwargs) + + def reset_state(self): + if not self.allow_early_resets and not self.needs_reset: + raise RuntimeError("Tried to reset an environment before done. If you want to allow early resets, wrap your env with Monitor(env, path, allow_early_resets=True)") + self.rewards = [] + self.needs_reset = False + + + def step(self, action): + if self.needs_reset: + raise RuntimeError("Tried to step environment that needs reset") + ob, rew, done, info = self.env.step(action) + self.update(ob, rew, done, info) + return (ob, rew, done, info) + + def update(self, ob, rew, done, info): + self.rewards.append(rew) + if done: + self.needs_reset = True + eprew = sum(self.rewards) + eplen = len(self.rewards) + epinfo = {"r": round(eprew, 6), "l": eplen, "t": round(time.time() - self.tstart, 6)} + for k in self.info_keywords: + epinfo[k] = info[k] + self.episode_rewards.append(eprew) + self.episode_lengths.append(eplen) + self.episode_times.append(time.time() - self.tstart) + epinfo.update(self.current_reset_info) + if self.results_writer: + self.results_writer.write_row(epinfo) + assert isinstance(info, dict) + if isinstance(info, dict): + info['episode'] = epinfo + + self.total_steps += 1 + + def close(self): + if self.f is not None: + self.f.close() + + def get_total_steps(self): + return self.total_steps + + def get_episode_rewards(self): + return self.episode_rewards + + def get_episode_lengths(self): + return self.episode_lengths + + def get_episode_times(self): + return self.episode_times + +class LoadMonitorResultsError(Exception): + pass + + +class ResultsWriter(object): + def __init__(self, filename, header='', extra_keys=()): + self.extra_keys = extra_keys + assert filename is not None + if not filename.endswith(Monitor.EXT): + if osp.isdir(filename): + filename = osp.join(filename, Monitor.EXT) + else: + filename = filename + "." + Monitor.EXT + self.f = open(filename, "wt") + if isinstance(header, dict): + header = '# {} \n'.format(json.dumps(header)) + self.f.write(header) + self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+tuple(extra_keys)) + self.logger.writeheader() + self.f.flush() + + def write_row(self, epinfo): + if self.logger: + self.logger.writerow(epinfo) + self.f.flush() + + +def get_monitor_files(dir): + return glob(osp.join(dir, "*" + Monitor.EXT)) + +def load_results(dir): + import pandas + monitor_files = ( + glob(osp.join(dir, "*monitor.json")) + + glob(osp.join(dir, "*monitor.csv"))) # get both csv and (old) json files + if not monitor_files: + raise LoadMonitorResultsError("no monitor files of the form *%s found in %s" % (Monitor.EXT, dir)) + dfs = [] + headers = [] + for fname in monitor_files: + with open(fname, 'rt') as fh: + if fname.endswith('csv'): + firstline = fh.readline() + if not firstline: + continue + assert firstline[0] == '#' + header = json.loads(firstline[1:]) + df = pandas.read_csv(fh, index_col=None) + headers.append(header) + elif fname.endswith('json'): # Deprecated json format + episodes = [] + lines = fh.readlines() + header = json.loads(lines[0]) + headers.append(header) + for line in lines[1:]: + episode = json.loads(line) + episodes.append(episode) + df = pandas.DataFrame(episodes) + else: + assert 0, 'unreachable' + df['t'] += header['t_start'] + dfs.append(df) + df = pandas.concat(dfs) + df.sort_values('t', inplace=True) + df.reset_index(inplace=True) + df['t'] -= min(header['t_start'] for header in headers) + df.headers = headers # HACK to preserve backwards compatibility + return df diff --git a/baselines/bench/test_monitor.py b/baselines/bench/test_monitor.py new file mode 100644 index 0000000000000000000000000000000000000000..093f9c6fd523b46c95f2468d54f1469fa086778b --- /dev/null +++ b/baselines/bench/test_monitor.py @@ -0,0 +1,31 @@ +from .monitor import Monitor +import gym +import json + +def test_monitor(): + import pandas + import os + import uuid + + env = gym.make("CartPole-v1") + env.seed(0) + mon_file = "/tmp/baselines-test-%s.monitor.csv" % uuid.uuid4() + menv = Monitor(env, mon_file) + menv.reset() + for _ in range(1000): + _, _, done, _ = menv.step(0) + if done: + menv.reset() + + f = open(mon_file, 'rt') + + firstline = f.readline() + assert firstline.startswith('#') + metadata = json.loads(firstline[1:]) + assert metadata['env_id'] == "CartPole-v1" + assert set(metadata.keys()) == {'env_id', 't_start'}, "Incorrect keys in monitor metadata" + + last_logline = pandas.read_csv(f, index_col=None) + assert set(last_logline.keys()) == {'l', 't', 'r'}, "Incorrect keys in monitor logline" + f.close() + os.remove(mon_file) diff --git a/baselines/common/__init__.py b/baselines/common/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0834b3649275b3b0607acde57f83983155c54ecc --- /dev/null +++ b/baselines/common/__init__.py @@ -0,0 +1,5 @@ +# flake8: noqa F403 +from baselines.common.console_util import * +from baselines.common.dataset import Dataset +from baselines.common.math_util import * +from baselines.common.misc_util import * diff --git a/baselines/common/atari_wrappers.py b/baselines/common/atari_wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..8be0a2f2542c5d88e3638cf068502b1df58c862b --- /dev/null +++ b/baselines/common/atari_wrappers.py @@ -0,0 +1,290 @@ +import numpy as np +import os +os.environ.setdefault('PATH', '') +from collections import deque +import gym +from gym import spaces +import cv2 +cv2.ocl.setUseOpenCL(False) +from .wrappers import TimeLimit + + +class NoopResetEnv(gym.Wrapper): + def __init__(self, env, noop_max=30): + """Sample initial states by taking random number of no-ops on reset. + No-op is assumed to be action 0. + """ + gym.Wrapper.__init__(self, env) + self.noop_max = noop_max + self.override_num_noops = None + self.noop_action = 0 + assert env.unwrapped.get_action_meanings()[0] == 'NOOP' + + def reset(self, **kwargs): + """ Do no-op action for a number of steps in [1, noop_max].""" + self.env.reset(**kwargs) + if self.override_num_noops is not None: + noops = self.override_num_noops + else: + noops = self.unwrapped.np_random.randint(1, self.noop_max + 1) #pylint: disable=E1101 + assert noops > 0 + obs = None + for _ in range(noops): + obs, _, done, _ = self.env.step(self.noop_action) + if done: + obs = self.env.reset(**kwargs) + return obs + + def step(self, ac): + return self.env.step(ac) + +class FireResetEnv(gym.Wrapper): + def __init__(self, env): + """Take action on reset for environments that are fixed until firing.""" + gym.Wrapper.__init__(self, env) + assert env.unwrapped.get_action_meanings()[1] == 'FIRE' + assert len(env.unwrapped.get_action_meanings()) >= 3 + + def reset(self, **kwargs): + self.env.reset(**kwargs) + obs, _, done, _ = self.env.step(1) + if done: + self.env.reset(**kwargs) + obs, _, done, _ = self.env.step(2) + if done: + self.env.reset(**kwargs) + return obs + + def step(self, ac): + return self.env.step(ac) + +class EpisodicLifeEnv(gym.Wrapper): + def __init__(self, env): + """Make end-of-life == end-of-episode, but only reset on true game over. + Done by DeepMind for the DQN and co. since it helps value estimation. + """ + gym.Wrapper.__init__(self, env) + self.lives = 0 + self.was_real_done = True + + def step(self, action): + obs, reward, done, info = self.env.step(action) + self.was_real_done = done + # check current lives, make loss of life terminal, + # then update lives to handle bonus lives + lives = self.env.unwrapped.ale.lives() + if lives < self.lives and lives > 0: + # for Qbert sometimes we stay in lives == 0 condition for a few frames + # so it's important to keep lives > 0, so that we only reset once + # the environment advertises done. + done = True + self.lives = lives + return obs, reward, done, info + + def reset(self, **kwargs): + """Reset only when lives are exhausted. + This way all states are still reachable even though lives are episodic, + and the learner need not know about any of this behind-the-scenes. + """ + if self.was_real_done: + obs = self.env.reset(**kwargs) + else: + # no-op step to advance from terminal/lost life state + obs, _, _, _ = self.env.step(0) + self.lives = self.env.unwrapped.ale.lives() + return obs + +class MaxAndSkipEnv(gym.Wrapper): + def __init__(self, env, skip=4): + """Return only every `skip`-th frame""" + gym.Wrapper.__init__(self, env) + # most recent raw observations (for max pooling across time steps) + self._obs_buffer = np.zeros((2,)+env.observation_space.shape, dtype=np.uint8) + self._skip = skip + + def step(self, action): + """Repeat action, sum reward, and max over last observations.""" + total_reward = 0.0 + done = None + for i in range(self._skip): + obs, reward, done, info = self.env.step(action) + if i == self._skip - 2: self._obs_buffer[0] = obs + if i == self._skip - 1: self._obs_buffer[1] = obs + total_reward += reward + if done: + break + # Note that the observation on the done=True frame + # doesn't matter + max_frame = self._obs_buffer.max(axis=0) + + return max_frame, total_reward, done, info + + def reset(self, **kwargs): + return self.env.reset(**kwargs) + +class ClipRewardEnv(gym.RewardWrapper): + def __init__(self, env): + gym.RewardWrapper.__init__(self, env) + + def reward(self, reward): + """Bin reward to {+1, 0, -1} by its sign.""" + return np.sign(reward) + + +class WarpFrame(gym.ObservationWrapper): + def __init__(self, env, width=84, height=84, grayscale=True, dict_space_key=None): + """ + Warp frames to 84x84 as done in the Nature paper and later work. + + If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which + observation should be warped. + """ + super().__init__(env) + self._width = width + self._height = height + self._grayscale = grayscale + self._key = dict_space_key + if self._grayscale: + num_colors = 1 + else: + num_colors = 3 + + new_space = gym.spaces.Box( + low=0, + high=255, + shape=(self._height, self._width, num_colors), + dtype=np.uint8, + ) + if self._key is None: + original_space = self.observation_space + self.observation_space = new_space + else: + original_space = self.observation_space.spaces[self._key] + self.observation_space.spaces[self._key] = new_space + assert original_space.dtype == np.uint8 and len(original_space.shape) == 3 + + def observation(self, obs): + if self._key is None: + frame = obs + else: + frame = obs[self._key] + + if self._grayscale: + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) + frame = cv2.resize( + frame, (self._width, self._height), interpolation=cv2.INTER_AREA + ) + if self._grayscale: + frame = np.expand_dims(frame, -1) + + if self._key is None: + obs = frame + else: + obs = obs.copy() + obs[self._key] = frame + return obs + + +class FrameStack(gym.Wrapper): + def __init__(self, env, k): + """Stack k last frames. + + Returns lazy array, which is much more memory efficient. + + See Also + -------- + baselines.common.atari_wrappers.LazyFrames + """ + gym.Wrapper.__init__(self, env) + self.k = k + self.frames = deque([], maxlen=k) + shp = env.observation_space.shape + self.observation_space = spaces.Box(low=0, high=255, shape=(shp[:-1] + (shp[-1] * k,)), dtype=env.observation_space.dtype) + + def reset(self): + ob = self.env.reset() + for _ in range(self.k): + self.frames.append(ob) + return self._get_ob() + + def step(self, action): + ob, reward, done, info = self.env.step(action) + self.frames.append(ob) + return self._get_ob(), reward, done, info + + def _get_ob(self): + assert len(self.frames) == self.k + return LazyFrames(list(self.frames)) + +class ScaledFloatFrame(gym.ObservationWrapper): + def __init__(self, env): + gym.ObservationWrapper.__init__(self, env) + self.observation_space = gym.spaces.Box(low=0, high=1, shape=env.observation_space.shape, dtype=np.float32) + + def observation(self, observation): + # careful! This undoes the memory optimization, use + # with smaller replay buffers only. + return np.array(observation).astype(np.float32) / 255.0 + +class LazyFrames(object): + def __init__(self, frames): + """This object ensures that common frames between the observations are only stored once. + It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay + buffers. + + This object should only be converted to numpy array before being passed to the model. + + You'd not believe how complex the previous solution was.""" + self._frames = frames + self._out = None + + def _force(self): + if self._out is None: + self._out = np.concatenate(self._frames, axis=-1) + self._frames = None + return self._out + + def __array__(self, dtype=None): + out = self._force() + if dtype is not None: + out = out.astype(dtype) + return out + + def __len__(self): + return len(self._force()) + + def __getitem__(self, i): + return self._force()[i] + + def count(self): + frames = self._force() + return frames.shape[frames.ndim - 1] + + def frame(self, i): + return self._force()[..., i] + +def make_atari(env_id, max_episode_steps=None): + env = gym.make(env_id) + assert 'NoFrameskip' in env.spec.id + env = NoopResetEnv(env, noop_max=30) + env = MaxAndSkipEnv(env, skip=4) + if max_episode_steps is not None: + env = TimeLimit(env, max_episode_steps=max_episode_steps) + return env + +def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False): + """Configure environment for DeepMind-style Atari. + """ + if episode_life: + env = EpisodicLifeEnv(env) + if 'FIRE' in env.unwrapped.get_action_meanings(): + env = FireResetEnv(env) + env = WarpFrame(env) + if scale: + env = ScaledFloatFrame(env) + if clip_rewards: + env = ClipRewardEnv(env) + if frame_stack: + env = FrameStack(env, 4) + return env + diff --git a/baselines/common/cg.py b/baselines/common/cg.py new file mode 100644 index 0000000000000000000000000000000000000000..52ca49dc865799c69c746b11e5f8813ba044ef8c --- /dev/null +++ b/baselines/common/cg.py @@ -0,0 +1,34 @@ +import numpy as np +def cg(f_Ax, b, cg_iters=10, callback=None, verbose=False, residual_tol=1e-10): + """ + Demmel p 312 + """ + p = b.copy() + r = b.copy() + x = np.zeros_like(b) + rdotr = r.dot(r) + + fmtstr = "%10i %10.3g %10.3g" + titlestr = "%10s %10s %10s" + if verbose: print(titlestr % ("iter", "residual norm", "soln norm")) + + for i in range(cg_iters): + if callback is not None: + callback(x) + if verbose: print(fmtstr % (i, rdotr, np.linalg.norm(x))) + z = f_Ax(p) + v = rdotr / p.dot(z) + x += v*p + r -= v*z + newrdotr = r.dot(r) + mu = newrdotr/rdotr + p = r + mu*p + + rdotr = newrdotr + if rdotr < residual_tol: + break + + if callback is not None: + callback(x) + if verbose: print(fmtstr % (i+1, rdotr, np.linalg.norm(x))) # pylint: disable=W0631 + return x diff --git a/baselines/common/cmd_util.py b/baselines/common/cmd_util.py new file mode 100644 index 0000000000000000000000000000000000000000..bd6ef9b2901e7587544b2adf077d7d93d6807b36 --- /dev/null +++ b/baselines/common/cmd_util.py @@ -0,0 +1,206 @@ +""" +Helpers for scripts like run_atari.py. +""" + +import os +try: + from mpi4py import MPI +except ImportError: + MPI = None + +import gym +from gym.wrappers import FlattenObservation, FilterObservation +from baselines import logger +from baselines.bench import Monitor +from baselines.common import set_global_seeds +from baselines.common.atari_wrappers import make_atari, wrap_deepmind +from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv +from baselines.common.vec_env.dummy_vec_env import DummyVecEnv +from baselines.common import retro_wrappers +from baselines.common.wrappers import ClipActionsWrapper + +def make_vec_env(env_id, env_type, num_env, seed, + wrapper_kwargs=None, + env_kwargs=None, + start_index=0, + reward_scale=1.0, + flatten_dict_observations=True, + gamestate=None, + initializer=None, + force_dummy=False): + """ + Create a wrapped, monitored SubprocVecEnv for Atari and MuJoCo. + """ + wrapper_kwargs = wrapper_kwargs or {} + env_kwargs = env_kwargs or {} + mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0 + seed = seed + 10000 * mpi_rank if seed is not None else None + logger_dir = logger.get_dir() + def make_thunk(rank, initializer=None): + return lambda: make_env( + env_id=env_id, + env_type=env_type, + mpi_rank=mpi_rank, + subrank=rank, + seed=seed, + reward_scale=reward_scale, + gamestate=gamestate, + flatten_dict_observations=flatten_dict_observations, + wrapper_kwargs=wrapper_kwargs, + env_kwargs=env_kwargs, + logger_dir=logger_dir, + initializer=initializer + ) + + set_global_seeds(seed) + if not force_dummy and num_env > 1: + return SubprocVecEnv([make_thunk(i + start_index, initializer=initializer) for i in range(num_env)]) + else: + return DummyVecEnv([make_thunk(i + start_index, initializer=None) for i in range(num_env)]) + + +def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.0, gamestate=None, flatten_dict_observations=True, wrapper_kwargs=None, env_kwargs=None, logger_dir=None, initializer=None): + if initializer is not None: + initializer(mpi_rank=mpi_rank, subrank=subrank) + + wrapper_kwargs = wrapper_kwargs or {} + env_kwargs = env_kwargs or {} + if ':' in env_id: + import re + import importlib + module_name = re.sub(':.*','',env_id) + env_id = re.sub('.*:', '', env_id) + importlib.import_module(module_name) + if env_type == 'atari': + env = make_atari(env_id) + elif env_type == 'retro': + import retro + gamestate = gamestate or retro.State.DEFAULT + env = retro_wrappers.make_retro(game=env_id, max_episode_steps=10000, use_restricted_actions=retro.Actions.DISCRETE, state=gamestate) + else: + env = gym.make(env_id, **env_kwargs) + + if flatten_dict_observations and isinstance(env.observation_space, gym.spaces.Dict): + env = FlattenObservation(env) + + env.seed(seed + subrank if seed is not None else None) + env = Monitor(env, + logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)), + allow_early_resets=True) + + + if env_type == 'atari': + env = wrap_deepmind(env, **wrapper_kwargs) + elif env_type == 'retro': + if 'frame_stack' not in wrapper_kwargs: + wrapper_kwargs['frame_stack'] = 1 + env = retro_wrappers.wrap_deepmind_retro(env, **wrapper_kwargs) + + if isinstance(env.action_space, gym.spaces.Box): + env = ClipActionsWrapper(env) + + if reward_scale != 1: + env = retro_wrappers.RewardScaler(env, reward_scale) + + return env + + +def make_mujoco_env(env_id, seed, reward_scale=1.0): + """ + Create a wrapped, monitored gym.Env for MuJoCo. + """ + rank = MPI.COMM_WORLD.Get_rank() + myseed = seed + 1000 * rank if seed is not None else None + set_global_seeds(myseed) + env = gym.make(env_id) + logger_path = None if logger.get_dir() is None else os.path.join(logger.get_dir(), str(rank)) + env = Monitor(env, logger_path, allow_early_resets=True) + env.seed(seed) + if reward_scale != 1.0: + from baselines.common.retro_wrappers import RewardScaler + env = RewardScaler(env, reward_scale) + return env + +def make_robotics_env(env_id, seed, rank=0): + """ + Create a wrapped, monitored gym.Env for MuJoCo. + """ + set_global_seeds(seed) + env = gym.make(env_id) + env = FlattenObservation(FilterObservation(env, ['observation', 'desired_goal'])) + env = Monitor( + env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)), + info_keywords=('is_success',)) + env.seed(seed) + return env + +def arg_parser(): + """ + Create an empty argparse.ArgumentParser. + """ + import argparse + return argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + +def atari_arg_parser(): + """ + Create an argparse.ArgumentParser for run_atari.py. + """ + print('Obsolete - use common_arg_parser instead') + return common_arg_parser() + +def mujoco_arg_parser(): + print('Obsolete - use common_arg_parser instead') + return common_arg_parser() + +def common_arg_parser(): + """ + Create an argparse.ArgumentParser for run_mujoco.py. + """ + parser = arg_parser() + parser.add_argument('--env', help='environment ID', type=str, default='Reacher-v2') + parser.add_argument('--env_type', help='type of environment, used when the environment type cannot be automatically determined', type=str) + parser.add_argument('--seed', help='RNG seed', type=int, default=None) + parser.add_argument('--alg', help='Algorithm', type=str, default='ppo2') + parser.add_argument('--num_timesteps', type=float, default=1e6), + parser.add_argument('--network', help='network type (mlp, cnn, lstm, cnn_lstm, conv_only)', default=None) + parser.add_argument('--gamestate', help='game state to load (so far only used in retro games)', default=None) + parser.add_argument('--num_env', help='Number of environment copies being run in parallel. When not specified, set to number of cpus for Atari, and to 1 for Mujoco', default=None, type=int) + parser.add_argument('--reward_scale', help='Reward scale factor. Default: 1.0', default=1.0, type=float) + parser.add_argument('--save_path', help='Path to save trained model to', default=None, type=str) + parser.add_argument('--save_video_interval', help='Save video every x steps (0 = disabled)', default=0, type=int) + parser.add_argument('--save_video_length', help='Length of recorded video. Default: 200', default=200, type=int) + parser.add_argument('--log_path', help='Directory to save learning curve data.', default=None, type=str) + parser.add_argument('--play', default=False, action='store_true') + return parser + +def robotics_arg_parser(): + """ + Create an argparse.ArgumentParser for run_mujoco.py. + """ + parser = arg_parser() + parser.add_argument('--env', help='environment ID', type=str, default='FetchReach-v0') + parser.add_argument('--seed', help='RNG seed', type=int, default=None) + parser.add_argument('--num-timesteps', type=int, default=int(1e6)) + return parser + + +def parse_unknown_args(args): + """ + Parse arguments not consumed by arg parser into a dictionary + """ + retval = {} + preceded_by_key = False + for arg in args: + if arg.startswith('--'): + if '=' in arg: + key = arg.split('=')[0][2:] + value = arg.split('=')[1] + retval[key] = value + else: + key = arg[2:] + preceded_by_key = True + elif preceded_by_key: + retval[key] = arg + preceded_by_key = False + + return retval diff --git a/baselines/common/console_util.py b/baselines/common/console_util.py new file mode 100644 index 0000000000000000000000000000000000000000..3b011c59750ca38a3fc96f73735b61f72c04db02 --- /dev/null +++ b/baselines/common/console_util.py @@ -0,0 +1,80 @@ +from __future__ import print_function +from contextlib import contextmanager +import numpy as np +import time +import shlex +import subprocess + +# ================================================================ +# Misc +# ================================================================ + +def fmt_row(width, row, header=False): + out = " | ".join(fmt_item(x, width) for x in row) + if header: out = out + "\n" + "-"*len(out) + return out + +def fmt_item(x, l): + if isinstance(x, np.ndarray): + assert x.ndim==0 + x = x.item() + if isinstance(x, (float, np.float32, np.float64)): + v = abs(x) + if (v < 1e-4 or v > 1e+4) and v > 0: + rep = "%7.2e" % x + else: + rep = "%7.5f" % x + else: rep = str(x) + return " "*(l - len(rep)) + rep + +color2num = dict( + gray=30, + red=31, + green=32, + yellow=33, + blue=34, + magenta=35, + cyan=36, + white=37, + crimson=38 +) + +def colorize(string, color='green', bold=False, highlight=False): + attr = [] + num = color2num[color] + if highlight: num += 10 + attr.append(str(num)) + if bold: attr.append('1') + return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string) + +def print_cmd(cmd, dry=False): + if isinstance(cmd, str): # for shell=True + pass + else: + cmd = ' '.join(shlex.quote(arg) for arg in cmd) + print(colorize(('CMD: ' if not dry else 'DRY: ') + cmd)) + + +def get_git_commit(cwd=None): + return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], cwd=cwd).decode('utf8') + +def get_git_commit_message(cwd=None): + return subprocess.check_output(['git', 'show', '-s', '--format=%B', 'HEAD'], cwd=cwd).decode('utf8') + +def ccap(cmd, dry=False, env=None, **kwargs): + print_cmd(cmd, dry) + if not dry: + subprocess.check_call(cmd, env=env, **kwargs) + + +MESSAGE_DEPTH = 0 + +@contextmanager +def timed(msg): + global MESSAGE_DEPTH #pylint: disable=W0603 + print(colorize('\t'*MESSAGE_DEPTH + '=: ' + msg, color='magenta')) + tstart = time.time() + MESSAGE_DEPTH += 1 + yield + MESSAGE_DEPTH -= 1 + print(colorize('\t'*MESSAGE_DEPTH + "done in %.3f seconds"%(time.time() - tstart), color='magenta')) diff --git a/baselines/common/dataset.py b/baselines/common/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..41a38c8af68cd4c86cc01e862e8a4193b98c7e1a --- /dev/null +++ b/baselines/common/dataset.py @@ -0,0 +1,60 @@ +import numpy as np + +class Dataset(object): + def __init__(self, data_map, deterministic=False, shuffle=True): + self.data_map = data_map + self.deterministic = deterministic + self.enable_shuffle = shuffle + self.n = next(iter(data_map.values())).shape[0] + self._next_id = 0 + self.shuffle() + + def shuffle(self): + if self.deterministic: + return + perm = np.arange(self.n) + np.random.shuffle(perm) + + for key in self.data_map: + self.data_map[key] = self.data_map[key][perm] + + self._next_id = 0 + + def next_batch(self, batch_size): + if self._next_id >= self.n and self.enable_shuffle: + self.shuffle() + + cur_id = self._next_id + cur_batch_size = min(batch_size, self.n - self._next_id) + self._next_id += cur_batch_size + + data_map = dict() + for key in self.data_map: + data_map[key] = self.data_map[key][cur_id:cur_id+cur_batch_size] + return data_map + + def iterate_once(self, batch_size): + if self.enable_shuffle: self.shuffle() + + while self._next_id <= self.n - batch_size: + yield self.next_batch(batch_size) + self._next_id = 0 + + def subset(self, num_elements, deterministic=True): + data_map = dict() + for key in self.data_map: + data_map[key] = self.data_map[key][:num_elements] + return Dataset(data_map, deterministic) + + +def iterbatches(arrays, *, num_batches=None, batch_size=None, shuffle=True, include_final_partial_batch=True): + assert (num_batches is None) != (batch_size is None), 'Provide num_batches or batch_size, but not both' + arrays = tuple(map(np.asarray, arrays)) + n = arrays[0].shape[0] + assert all(a.shape[0] == n for a in arrays[1:]) + inds = np.arange(n) + if shuffle: np.random.shuffle(inds) + sections = np.arange(0, n, batch_size)[1:] if num_batches is None else num_batches + for batch_inds in np.array_split(inds, sections): + if include_final_partial_batch or len(batch_inds) == batch_size: + yield tuple(a[batch_inds] for a in arrays) diff --git a/baselines/common/distributions.py b/baselines/common/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..b70ec306c529544e205db34201704a10915685e0 --- /dev/null +++ b/baselines/common/distributions.py @@ -0,0 +1,355 @@ +import tensorflow as tf +import numpy as np +import baselines.common.tf_util as U +from baselines.a2c.utils import fc +from tensorflow.python.ops import math_ops + +class Pd(object): + """ + A particular probability distribution + """ + def flatparam(self): + raise NotImplementedError + def mode(self): + raise NotImplementedError + def neglogp(self, x): + # Usually it's easier to define the negative logprob + raise NotImplementedError + def kl(self, other): + raise NotImplementedError + def entropy(self): + raise NotImplementedError + def sample(self): + raise NotImplementedError + def logp(self, x): + return - self.neglogp(x) + def get_shape(self): + return self.flatparam().shape + @property + def shape(self): + return self.get_shape() + def __getitem__(self, idx): + return self.__class__(self.flatparam()[idx]) + +class PdType(object): + """ + Parametrized family of probability distributions + """ + def pdclass(self): + raise NotImplementedError + def pdfromflat(self, flat): + return self.pdclass()(flat) + def pdfromlatent(self, latent_vector, init_scale, init_bias): + raise NotImplementedError + def param_shape(self): + raise NotImplementedError + def sample_shape(self): + raise NotImplementedError + def sample_dtype(self): + raise NotImplementedError + + def param_placeholder(self, prepend_shape, name=None): + return tf.compat.v1.placeholder(dtype=tf.float32, shape=prepend_shape+self.param_shape(), name=name) + def sample_placeholder(self, prepend_shape, name=None): + return tf.compat.v1.placeholder(dtype=self.sample_dtype(), shape=prepend_shape+self.sample_shape(), name=name) + + def __eq__(self, other): + return (type(self) == type(other)) and (self.__dict__ == other.__dict__) + +class CategoricalPdType(PdType): + def __init__(self, ncat): + self.ncat = ncat + def pdclass(self): + return CategoricalPd + def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0): + pdparam = _matching_fc(latent_vector, 'pi', self.ncat, init_scale=init_scale, init_bias=init_bias) + return self.pdfromflat(pdparam), pdparam + + def param_shape(self): + return [self.ncat] + def sample_shape(self): + return [] + def sample_dtype(self): + return tf.int32 + + +class MultiCategoricalPdType(PdType): + def __init__(self, nvec): + self.ncats = nvec.astype('int32') + assert (self.ncats > 0).all() + def pdclass(self): + return MultiCategoricalPd + def pdfromflat(self, flat): + return MultiCategoricalPd(self.ncats, flat) + + def pdfromlatent(self, latent, init_scale=1.0, init_bias=0.0): + pdparam = _matching_fc(latent, 'pi', self.ncats.sum(), init_scale=init_scale, init_bias=init_bias) + return self.pdfromflat(pdparam), pdparam + + def param_shape(self): + return [sum(self.ncats)] + def sample_shape(self): + return [len(self.ncats)] + def sample_dtype(self): + return tf.int32 + +class DiagGaussianPdType(PdType): + def __init__(self, size): + self.size = size + def pdclass(self): + return DiagGaussianPd + + def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0): + mean = _matching_fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias) + logstd = tf.compat.v1.get_variable(name='pi/logstd', shape=[1, self.size], initializer=tf.compat.v1.zeros_initializer()) + pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1) + return self.pdfromflat(pdparam), mean + + def param_shape(self): + return [2*self.size] + def sample_shape(self): + return [self.size] + def sample_dtype(self): + return tf.float32 + +class BernoulliPdType(PdType): + def __init__(self, size): + self.size = size + def pdclass(self): + return BernoulliPd + def param_shape(self): + return [self.size] + def sample_shape(self): + return [self.size] + def sample_dtype(self): + return tf.int32 + def pdfromlatent(self, latent_vector, init_scale=1.0, init_bias=0.0): + pdparam = _matching_fc(latent_vector, 'pi', self.size, init_scale=init_scale, init_bias=init_bias) + return self.pdfromflat(pdparam), pdparam + +# WRONG SECOND DERIVATIVES +# class CategoricalPd(Pd): +# def __init__(self, logits): +# self.logits = logits +# self.ps = tf.nn.softmax(logits) +# @classmethod +# def fromflat(cls, flat): +# return cls(flat) +# def flatparam(self): +# return self.logits +# def mode(self): +# return U.argmax(self.logits, axis=-1) +# def logp(self, x): +# return -tf.nn.sparse_softmax_cross_entropy_with_logits(self.logits, x) +# def kl(self, other): +# return tf.nn.softmax_cross_entropy_with_logits(other.logits, self.ps) \ +# - tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps) +# def entropy(self): +# return tf.nn.softmax_cross_entropy_with_logits(self.logits, self.ps) +# def sample(self): +# u = tf.random_uniform(tf.shape(self.logits)) +# return U.argmax(self.logits - tf.log(-tf.log(u)), axis=-1) + +class CategoricalPd(Pd): + def __init__(self, logits): + self.logits = logits + def flatparam(self): + return self.logits + def mode(self): + return tf.argmax(input=self.logits, axis=-1) + + @property + def mean(self): + return tf.nn.softmax(self.logits) + def neglogp(self, x): + # return tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=x) + # Note: we can't use sparse_softmax_cross_entropy_with_logits because + # the implementation does not allow second-order derivatives... + if x.dtype in {tf.uint8, tf.int32, tf.int64}: + # one-hot encoding + x_shape_list = x.shape.as_list() + logits_shape_list = self.logits.get_shape().as_list()[:-1] + for xs, ls in zip(x_shape_list, logits_shape_list): + if xs is not None and ls is not None: + assert xs == ls, 'shape mismatch: {} in x vs {} in logits'.format(xs, ls) + + x = tf.one_hot(x, self.logits.get_shape().as_list()[-1]) + else: + # already encoded + assert x.shape.as_list() == self.logits.shape.as_list() + + return tf.nn.softmax_cross_entropy_with_logits( + logits=self.logits, + labels=x) + def kl(self, other): + a0 = self.logits - tf.reduce_max(input_tensor=self.logits, axis=-1, keepdims=True) + a1 = other.logits - tf.reduce_max(input_tensor=other.logits, axis=-1, keepdims=True) + ea0 = tf.exp(a0) + ea1 = tf.exp(a1) + z0 = tf.reduce_sum(input_tensor=ea0, axis=-1, keepdims=True) + z1 = tf.reduce_sum(input_tensor=ea1, axis=-1, keepdims=True) + p0 = ea0 / z0 + return tf.reduce_sum(input_tensor=p0 * (a0 - tf.math.log(z0) - a1 + tf.math.log(z1)), axis=-1) + def entropy(self): + a0 = self.logits - tf.reduce_max(input_tensor=self.logits, axis=-1, keepdims=True) + ea0 = tf.exp(a0) + z0 = tf.reduce_sum(input_tensor=ea0, axis=-1, keepdims=True) + p0 = ea0 / z0 + return tf.reduce_sum(input_tensor=p0 * (tf.math.log(z0) - a0), axis=-1) + def sample(self): + u = tf.random.uniform(tf.shape(input=self.logits), dtype=self.logits.dtype) + return tf.argmax(input=self.logits - tf.math.log(-tf.math.log(u)), axis=-1) + @classmethod + def fromflat(cls, flat): + return cls(flat) + +class MultiCategoricalPd(Pd): + def __init__(self, nvec, flat): + self.flat = flat + self.categoricals = list(map(CategoricalPd, + tf.split(flat, np.array(nvec, dtype=np.int32), axis=-1))) + def flatparam(self): + return self.flat + def mode(self): + return tf.cast(tf.stack([p.mode() for p in self.categoricals], axis=-1), tf.int32) + def neglogp(self, x): + return tf.add_n([p.neglogp(px) for p, px in zip(self.categoricals, tf.unstack(x, axis=-1))]) + def kl(self, other): + return tf.add_n([p.kl(q) for p, q in zip(self.categoricals, other.categoricals)]) + def entropy(self): + return tf.add_n([p.entropy() for p in self.categoricals]) + def sample(self): + return tf.cast(tf.stack([p.sample() for p in self.categoricals], axis=-1), tf.int32) + @classmethod + def fromflat(cls, flat): + raise NotImplementedError + +class DiagGaussianPd(Pd): + def __init__(self, flat): + self.flat = flat + mean, logstd = tf.split(axis=len(flat.shape)-1, num_or_size_splits=2, value=flat) + self.mean = mean + self.logstd = logstd + self.std = tf.exp(logstd) + def flatparam(self): + return self.flat + def mode(self): + return self.mean + def neglogp(self, x): + return 0.5 * tf.reduce_sum(input_tensor=tf.square((x - self.mean) / self.std), axis=-1) \ + + 0.5 * np.log(2.0 * np.pi) * tf.cast(tf.shape(input=x)[-1], dtype=tf.float32) \ + + tf.reduce_sum(input_tensor=self.logstd, axis=-1) + def kl(self, other): + assert isinstance(other, DiagGaussianPd) + return tf.reduce_sum(input_tensor=other.logstd - self.logstd + (tf.square(self.std) + tf.square(self.mean - other.mean)) / (2.0 * tf.square(other.std)) - 0.5, axis=-1) + def entropy(self): + return tf.reduce_sum(input_tensor=self.logstd + .5 * np.log(2.0 * np.pi * np.e), axis=-1) + def sample(self): + return self.mean + self.std * tf.random.normal(tf.shape(input=self.mean)) + @classmethod + def fromflat(cls, flat): + return cls(flat) + + +class BernoulliPd(Pd): + def __init__(self, logits): + self.logits = logits + self.ps = tf.sigmoid(logits) + def flatparam(self): + return self.logits + @property + def mean(self): + return self.ps + def mode(self): + return tf.round(self.ps) + def neglogp(self, x): + return tf.reduce_sum(input_tensor=tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits, labels=tf.cast(x, dtype=tf.float32)), axis=-1) + def kl(self, other): + return tf.reduce_sum(input_tensor=tf.nn.sigmoid_cross_entropy_with_logits(logits=other.logits, labels=self.ps), axis=-1) - tf.reduce_sum(input_tensor=tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits, labels=self.ps), axis=-1) + def entropy(self): + return tf.reduce_sum(input_tensor=tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits, labels=self.ps), axis=-1) + def sample(self): + u = tf.random.uniform(tf.shape(input=self.ps)) + return tf.cast(math_ops.less(u, self.ps), dtype=tf.float32) + @classmethod + def fromflat(cls, flat): + return cls(flat) + +def make_pdtype(ac_space): + from gym import spaces + if isinstance(ac_space, spaces.Box): + assert len(ac_space.shape) == 1 + return DiagGaussianPdType(ac_space.shape[0]) + elif isinstance(ac_space, spaces.Discrete): + return CategoricalPdType(ac_space.n) + elif isinstance(ac_space, spaces.MultiDiscrete): + return MultiCategoricalPdType(ac_space.nvec) + elif isinstance(ac_space, spaces.MultiBinary): + return BernoulliPdType(ac_space.n) + else: + raise NotImplementedError + +def shape_el(v, i): + maybe = v.get_shape()[i] + if maybe is not None: + return maybe + else: + return tf.shape(input=v)[i] + +@U.in_session +def test_probtypes(): + np.random.seed(0) + + pdparam_diag_gauss = np.array([-.2, .3, .4, -.5, .1, -.5, .1, 0.8]) + diag_gauss = DiagGaussianPdType(pdparam_diag_gauss.size // 2) #pylint: disable=E1101 + validate_probtype(diag_gauss, pdparam_diag_gauss) + + pdparam_categorical = np.array([-.2, .3, .5]) + categorical = CategoricalPdType(pdparam_categorical.size) #pylint: disable=E1101 + validate_probtype(categorical, pdparam_categorical) + + nvec = [1,2,3] + pdparam_multicategorical = np.array([-.2, .3, .5, .1, 1, -.1]) + multicategorical = MultiCategoricalPdType(nvec) #pylint: disable=E1101 + validate_probtype(multicategorical, pdparam_multicategorical) + + pdparam_bernoulli = np.array([-.2, .3, .5]) + bernoulli = BernoulliPdType(pdparam_bernoulli.size) #pylint: disable=E1101 + validate_probtype(bernoulli, pdparam_bernoulli) + + +def validate_probtype(probtype, pdparam): + N = 100000 + # Check to see if mean negative log likelihood == differential entropy + Mval = np.repeat(pdparam[None, :], N, axis=0) + M = probtype.param_placeholder([N]) + X = probtype.sample_placeholder([N]) + pd = probtype.pdfromflat(M) + calcloglik = U.function([X, M], pd.logp(X)) + calcent = U.function([M], pd.entropy()) + Xval = tf.compat.v1.get_default_session().run(pd.sample(), feed_dict={M:Mval}) + logliks = calcloglik(Xval, Mval) + entval_ll = - logliks.mean() #pylint: disable=E1101 + entval_ll_stderr = logliks.std() / np.sqrt(N) #pylint: disable=E1101 + entval = calcent(Mval).mean() #pylint: disable=E1101 + assert np.abs(entval - entval_ll) < 3 * entval_ll_stderr # within 3 sigmas + + # Check to see if kldiv[p,q] = - ent[p] - E_p[log q] + M2 = probtype.param_placeholder([N]) + pd2 = probtype.pdfromflat(M2) + q = pdparam + np.random.randn(pdparam.size) * 0.1 + Mval2 = np.repeat(q[None, :], N, axis=0) + calckl = U.function([M, M2], pd.kl(pd2)) + klval = calckl(Mval, Mval2).mean() #pylint: disable=E1101 + logliks = calcloglik(Xval, Mval2) + klval_ll = - entval - logliks.mean() #pylint: disable=E1101 + klval_ll_stderr = logliks.std() / np.sqrt(N) #pylint: disable=E1101 + assert np.abs(klval - klval_ll) < 3 * klval_ll_stderr # within 3 sigmas + print('ok on', probtype, pdparam) + + +def _matching_fc(tensor, name, size, init_scale, init_bias): + if tensor.shape[-1] == size: + return tensor + else: + return fc(tensor, name, size, init_scale=init_scale, init_bias=init_bias) diff --git a/baselines/common/input.py b/baselines/common/input.py new file mode 100644 index 0000000000000000000000000000000000000000..f518a0bc8f1e19f3e6dc214f39c646ebadd960da --- /dev/null +++ b/baselines/common/input.py @@ -0,0 +1,65 @@ +import numpy as np +import tensorflow as tf +tf.compat.v1.disable_eager_execution() +from gym.spaces import Discrete, Box, MultiDiscrete + +def observation_placeholder(ob_space, batch_size=None, name='Ob'): + ''' + Create placeholder to feed observations into of the size appropriate to the observation space + + Parameters: + ---------- + + ob_space: gym.Space observation space + + batch_size: int size of the batch to be fed into input. Can be left None in most cases. + + name: str name of the placeholder + + Returns: + ------- + + tensorflow placeholder tensor + ''' + + assert isinstance(ob_space, Discrete) or isinstance(ob_space, Box) or isinstance(ob_space, MultiDiscrete), \ + 'Can only deal with Discrete and Box observation spaces for now' + + dtype = ob_space.dtype + if dtype == np.int8: + dtype = np.uint8 + + return tf.compat.v1.placeholder(shape=(batch_size,) + ob_space.shape, dtype=dtype, name=name) + + +def observation_input(ob_space, batch_size=None, name='Ob'): + ''' + Create placeholder to feed observations into of the size appropriate to the observation space, and add input + encoder of the appropriate type. + ''' + + placeholder = observation_placeholder(ob_space, batch_size, name) + return placeholder, encode_observation(ob_space, placeholder) + +def encode_observation(ob_space, placeholder): + ''' + Encode input in the way that is appropriate to the observation space + + Parameters: + ---------- + + ob_space: gym.Space observation space + + placeholder: tf.placeholder observation input placeholder + ''' + if isinstance(ob_space, Discrete): + return tf.cast(tf.one_hot(placeholder, ob_space.n), dtype=tf.float32) + elif isinstance(ob_space, Box): + return tf.cast(placeholder, dtype=tf.float32) + elif isinstance(ob_space, MultiDiscrete): + placeholder = tf.cast(placeholder, tf.int32) + one_hots = [tf.cast(tf.one_hot(placeholder[..., i], ob_space.nvec[i]), dtype=tf.float32) for i in range(placeholder.shape[-1])] + return tf.concat(one_hots, axis=-1) + else: + raise NotImplementedError + diff --git a/baselines/common/math_util.py b/baselines/common/math_util.py new file mode 100644 index 0000000000000000000000000000000000000000..461bdb79908696e1d7b1b811f578d7216c883b71 --- /dev/null +++ b/baselines/common/math_util.py @@ -0,0 +1,85 @@ +import numpy as np +import scipy.signal + + +def discount(x, gamma): + """ + computes discounted sums along 0th dimension of x. + + inputs + ------ + x: ndarray + gamma: float + + outputs + ------- + y: ndarray with same shape as x, satisfying + + y[t] = x[t] + gamma*x[t+1] + gamma^2*x[t+2] + ... + gamma^k x[t+k], + where k = len(x) - t - 1 + + """ + assert x.ndim >= 1 + return scipy.signal.lfilter([1],[1,-gamma],x[::-1], axis=0)[::-1] + +def explained_variance(ypred,y): + """ + Computes fraction of variance that ypred explains about y. + Returns 1 - Var[y-ypred] / Var[y] + + interpretation: + ev=0 => might as well have predicted zero + ev=1 => perfect prediction + ev<0 => worse than just predicting zero + + """ + assert y.ndim == 1 and ypred.ndim == 1 + vary = np.var(y) + return np.nan if vary==0 else 1 - np.var(y-ypred)/vary + +def explained_variance_2d(ypred, y): + assert y.ndim == 2 and ypred.ndim == 2 + vary = np.var(y, axis=0) + out = 1 - np.var(y-ypred)/vary + out[vary < 1e-10] = 0 + return out + +def ncc(ypred, y): + return np.corrcoef(ypred, y)[1,0] + +def flatten_arrays(arrs): + return np.concatenate([arr.flat for arr in arrs]) + +def unflatten_vector(vec, shapes): + i=0 + arrs = [] + for shape in shapes: + size = np.prod(shape) + arr = vec[i:i+size].reshape(shape) + arrs.append(arr) + i += size + return arrs + +def discount_with_boundaries(X, New, gamma): + """ + X: 2d array of floats, time x features + New: 2d array of bools, indicating when a new episode has started + """ + Y = np.zeros_like(X) + T = X.shape[0] + Y[T-1] = X[T-1] + for t in range(T-2, -1, -1): + Y[t] = X[t] + gamma * Y[t+1] * (1 - New[t+1]) + return Y + +def test_discount_with_boundaries(): + gamma=0.9 + x = np.array([1.0, 2.0, 3.0, 4.0], 'float32') + starts = [1.0, 0.0, 0.0, 1.0] + y = discount_with_boundaries(x, starts, gamma) + assert np.allclose(y, [ + 1 + gamma * 2 + gamma**2 * 3, + 2 + gamma * 3, + 3, + 4 + ]) diff --git a/baselines/common/misc_util.py b/baselines/common/misc_util.py new file mode 100644 index 0000000000000000000000000000000000000000..5e0347676483dd6654fd1fc2c3c58cd94d6d206a --- /dev/null +++ b/baselines/common/misc_util.py @@ -0,0 +1,243 @@ +import gym +import numpy as np +import os +import pickle +import random +import tempfile +import zipfile + + +def zipsame(*seqs): + L = len(seqs[0]) + assert all(len(seq) == L for seq in seqs[1:]) + return zip(*seqs) + + +class EzPickle(object): + """Objects that are pickled and unpickled via their constructor + arguments. + + Example usage: + + class Dog(Animal, EzPickle): + def __init__(self, furcolor, tailkind="bushy"): + Animal.__init__() + EzPickle.__init__(furcolor, tailkind) + ... + + When this object is unpickled, a new Dog will be constructed by passing the provided + furcolor and tailkind into the constructor. However, philosophers are still not sure + whether it is still the same dog. + + This is generally needed only for environments which wrap C/C++ code, such as MuJoCo + and Atari. + """ + + def __init__(self, *args, **kwargs): + self._ezpickle_args = args + self._ezpickle_kwargs = kwargs + + def __getstate__(self): + return {"_ezpickle_args": self._ezpickle_args, "_ezpickle_kwargs": self._ezpickle_kwargs} + + def __setstate__(self, d): + out = type(self)(*d["_ezpickle_args"], **d["_ezpickle_kwargs"]) + self.__dict__.update(out.__dict__) + + +def set_global_seeds(i): + try: + import MPI + rank = MPI.COMM_WORLD.Get_rank() + except ImportError: + rank = 0 + + myseed = i + 1000 * rank if i is not None else None + try: + import tensorflow as tf + tf.compat.v1.set_random_seed(myseed) + except ImportError: + pass + np.random.seed(myseed) + random.seed(myseed) + + +def pretty_eta(seconds_left): + """Print the number of seconds in human readable format. + + Examples: + 2 days + 2 hours and 37 minutes + less than a minute + + Paramters + --------- + seconds_left: int + Number of seconds to be converted to the ETA + Returns + ------- + eta: str + String representing the pretty ETA. + """ + minutes_left = seconds_left // 60 + seconds_left %= 60 + hours_left = minutes_left // 60 + minutes_left %= 60 + days_left = hours_left // 24 + hours_left %= 24 + + def helper(cnt, name): + return "{} {}{}".format(str(cnt), name, ('s' if cnt > 1 else '')) + + if days_left > 0: + msg = helper(days_left, 'day') + if hours_left > 0: + msg += ' and ' + helper(hours_left, 'hour') + return msg + if hours_left > 0: + msg = helper(hours_left, 'hour') + if minutes_left > 0: + msg += ' and ' + helper(minutes_left, 'minute') + return msg + if minutes_left > 0: + return helper(minutes_left, 'minute') + return 'less than a minute' + + +class RunningAvg(object): + def __init__(self, gamma, init_value=None): + """Keep a running estimate of a quantity. This is a bit like mean + but more sensitive to recent changes. + + Parameters + ---------- + gamma: float + Must be between 0 and 1, where 0 is the most sensitive to recent + changes. + init_value: float or None + Initial value of the estimate. If None, it will be set on the first update. + """ + self._value = init_value + self._gamma = gamma + + def update(self, new_val): + """Update the estimate. + + Parameters + ---------- + new_val: float + new observated value of estimated quantity. + """ + if self._value is None: + self._value = new_val + else: + self._value = self._gamma * self._value + (1.0 - self._gamma) * new_val + + def __float__(self): + """Get the current estimate""" + return self._value + +def boolean_flag(parser, name, default=False, help=None): + """Add a boolean flag to argparse parser. + + Parameters + ---------- + parser: argparse.Parser + parser to add the flag to + name: str + -- will enable the flag, while --no- will disable it + default: bool or None + default value of the flag + help: str + help string for the flag + """ + dest = name.replace('-', '_') + parser.add_argument("--" + name, action="store_true", default=default, dest=dest, help=help) + parser.add_argument("--no-" + name, action="store_false", dest=dest) + + +def get_wrapper_by_name(env, classname): + """Given an a gym environment possibly wrapped multiple times, returns a wrapper + of class named classname or raises ValueError if no such wrapper was applied + + Parameters + ---------- + env: gym.Env of gym.Wrapper + gym environment + classname: str + name of the wrapper + + Returns + ------- + wrapper: gym.Wrapper + wrapper named classname + """ + currentenv = env + while True: + if classname == currentenv.class_name(): + return currentenv + elif isinstance(currentenv, gym.Wrapper): + currentenv = currentenv.env + else: + raise ValueError("Couldn't find wrapper named %s" % classname) + + +def relatively_safe_pickle_dump(obj, path, compression=False): + """This is just like regular pickle dump, except from the fact that failure cases are + different: + + - It's never possible that we end up with a pickle in corrupted state. + - If a there was a different file at the path, that file will remain unchanged in the + even of failure (provided that filesystem rename is atomic). + - it is sometimes possible that we end up with useless temp file which needs to be + deleted manually (it will be removed automatically on the next function call) + + The indended use case is periodic checkpoints of experiment state, such that we never + corrupt previous checkpoints if the current one fails. + + Parameters + ---------- + obj: object + object to pickle + path: str + path to the output file + compression: bool + if true pickle will be compressed + """ + temp_storage = path + ".relatively_safe" + if compression: + # Using gzip here would be simpler, but the size is limited to 2GB + with tempfile.NamedTemporaryFile() as uncompressed_file: + pickle.dump(obj, uncompressed_file) + uncompressed_file.file.flush() + with zipfile.ZipFile(temp_storage, "w", compression=zipfile.ZIP_DEFLATED) as myzip: + myzip.write(uncompressed_file.name, "data") + else: + with open(temp_storage, "wb") as f: + pickle.dump(obj, f) + os.rename(temp_storage, path) + + +def pickle_load(path, compression=False): + """Unpickle a possible compressed pickle. + + Parameters + ---------- + path: str + path to the output file + compression: bool + if true assumes that pickle was compressed when created and attempts decompression. + + Returns + ------- + obj: object + the unpickled object + """ + + if compression: + with zipfile.ZipFile(path, "r", compression=zipfile.ZIP_DEFLATED) as myzip: + with myzip.open("data") as f: + return pickle.load(f) + else: + with open(path, "rb") as f: + return pickle.load(f) diff --git a/baselines/common/models.py b/baselines/common/models.py new file mode 100644 index 0000000000000000000000000000000000000000..7c3884852ef7f1b8d5d277ec0231c0b21e894da6 --- /dev/null +++ b/baselines/common/models.py @@ -0,0 +1,406 @@ +import math + +import numpy as np +import tensorflow as tf +from baselines.a2c import utils +from baselines.a2c.utils import conv, fc, conv_to_fc, batch_to_seq, seq_to_batch +from baselines.common.mpi_running_mean_std import RunningMeanStd +from keras import layers +from itertools import combinations + +mapping = {} + + +def register(name): + def _thunk(func): + mapping[name] = func + return func + + return _thunk + + +def nature_cnn(unscaled_images, **conv_kwargs): + """ + CNN from Nature paper. + """ + scaled_images = tf.cast(unscaled_images, tf.float32) / 255. + activ = tf.nn.relu + h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2), + **conv_kwargs)) + h2 = activ(conv(h, 'c2', nf=64, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs)) + h3 = activ(conv(h2, 'c3', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), **conv_kwargs)) + h3 = conv_to_fc(h3) + return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2))) + + +def build_impala_cnn(unscaled_images, depths=[16, 32, 32], **conv_kwargs): + """ + Model used in the paper "IMPALA: Scalable Distributed Deep-RL with + Importance Weighted Actor-Learner Architectures" https://arxiv.org/abs/1802.01561 + """ + + layer_num = 0 + + def get_layer_num_str(): + nonlocal layer_num + num_str = str(layer_num) + layer_num += 1 + return num_str + + def conv_layer(out, depth): + return tf.compat.v1.layers.conv2d(out, depth, 3, padding='same', name='layer_' + get_layer_num_str()) + + def residual_block(inputs): + try: + depth = inputs.get_shape()[-1].value + except: + depth = inputs.get_shape()[-1] + + out = tf.nn.relu(inputs) + + out = conv_layer(out, depth) + out = tf.nn.relu(out) + out = conv_layer(out, depth) + return out + inputs + + def conv_sequence(inputs, depth): + out = conv_layer(inputs, depth) + out = tf.compat.v1.layers.max_pooling2d(out, pool_size=3, strides=2, padding='same') + out = residual_block(out) + out = residual_block(out) + return out + + out = tf.cast(unscaled_images, tf.float32) / 255. + + for depth in depths: + out = conv_sequence(out, depth) + + out = tf.compat.v1.layers.flatten(out) + out = tf.nn.relu(out) + out = tf.compat.v1.layers.dense(out, 256, activation=tf.nn.relu, name='layer_' + get_layer_num_str()) + + return out + + +def build_skill_impala_cnn(unscaled_images, depths=[16, 32, 32], emb_dim=256, num_embeddings=8, seed=0, + **conv_kwargs): + """ + Modified impala cnn model by adding the skill module + """ + + layer_num = 0 + + def get_layer_num_str(): + nonlocal layer_num + num_str = str(layer_num) + layer_num += 1 + return num_str + + def conv_layer(out, depth): + return tf.compat.v1.layers.conv2d(out, depth, 3, padding='same', name='layer_' + get_layer_num_str()) + + def residual_block(inputs): + # depth = inputs.get_shape()[-1].value + depth = inputs.get_shape()[-1] + + out = tf.nn.relu(inputs) + + out = conv_layer(out, depth) + out = tf.nn.relu(out) + out = conv_layer(out, depth) + return out + inputs + + def conv_sequence(inputs, depth): + out = conv_layer(inputs, depth) + out = tf.compat.v1.layers.max_pooling2d(out, pool_size=3, strides=2, padding='same') + out = residual_block(out) + out = residual_block(out) + return out + + out = tf.cast(unscaled_images, tf.float32) / 255. + + for depth in depths: + out = conv_sequence(out, depth) + + out = tf.compat.v1.layers.flatten(out) + out = tf.nn.relu(out) + pure_out = tf.compat.v1.layers.dense(out, emb_dim, activation=tf.nn.relu, name='layer_' + get_layer_num_str()) + + # skill module + skill_out = tf.compat.v1.layers.dense(pure_out, emb_dim // 2, activation=None, name='layer_' + get_layer_num_str()) + skill_out = tf.compat.v1.layers.dense(skill_out, 2, activation=None, name='layer_' + get_layer_num_str()) + vq_layer = VectorQuantizer(num_embeddings, 2, seed=seed, name="vector_quantizer") + vq_out, pure_vq_out, encoding_indices = vq_layer(skill_out) + + encoding_indices_ = tf.cast( + tf.tile(encoding_indices / vq_layer.num_embeddings, tf.constant([1, emb_dim], tf.int32)), tf.float32) + + # add the normalized skill indices to features + out = tf.math.add(pure_out, encoding_indices_) + + return out, skill_out, pure_out, vq_out, pure_vq_out, vq_layer.embeddings, encoding_indices + + +@register("mlp") +def mlp(num_layers=2, num_hidden=64, activation=tf.tanh, layer_norm=False): + """ + Stack of fully-connected layers to be used in a policy / q-function approximator + + Parameters: + ---------- + + num_layers: int number of fully-connected layers (default: 2) + + num_hidden: int size of fully-connected layers (default: 64) + + activation: activation function (default: tf.tanh) + + Returns: + ------- + + function that builds fully connected network with a given input tensor / placeholder + """ + + def network_fn(X): + h = tf.compat.v1.layers.flatten(X) + for i in range(num_layers): + h = fc(h, 'mlp_fc{}'.format(i), nh=num_hidden, init_scale=np.sqrt(2)) + if layer_norm: + h = tf.contrib.layers.layer_norm(h, center=True, scale=True) + h = activation(h) + + return h + + return network_fn + + +@register("cnn") +def cnn(**conv_kwargs): + def network_fn(X): + return nature_cnn(X, **conv_kwargs) + + return network_fn + + +@register("impala_cnn") +def impala_cnn(**conv_kwargs): + def network_fn(X): + return build_impala_cnn(X) + + return network_fn + + +@register("cnn_small") +def cnn_small(**conv_kwargs): + def network_fn(X): + h = tf.cast(X, tf.float32) / 255. + + activ = tf.nn.relu + h = activ(conv(h, 'c1', nf=8, rf=8, stride=4, init_scale=np.sqrt(2), **conv_kwargs)) + h = activ(conv(h, 'c2', nf=16, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs)) + h = conv_to_fc(h) + h = activ(fc(h, 'fc1', nh=128, init_scale=np.sqrt(2))) + return h + + return network_fn + + +@register("lstm") +def lstm(nlstm=128, layer_norm=False): + """ + Builds LSTM (Long-Short Term Memory) network to be used in a policy. + Note that the resulting function returns not only the output of the LSTM + (i.e. hidden state of lstm for each step in the sequence), but also a dictionary + with auxiliary tensors to be set as policy attributes. + + Specifically, + S is a placeholder to feed current state (LSTM state has to be managed outside policy) + M is a placeholder for the mask (used to mask out observations after the end of the episode, but can be used for other purposes too) + initial_state is a numpy array containing initial lstm state (usually zeros) + state is the output LSTM state (to be fed into S at the next call) + + + An example of usage of lstm-based policy can be found here: common/tests/test_doc_examples.py/test_lstm_example + + Parameters: + ---------- + + nlstm: int LSTM hidden state size + + layer_norm: bool if True, layer-normalized version of LSTM is used + + Returns: + ------- + + function that builds LSTM with a given input tensor / placeholder + """ + + def network_fn(X, nenv=1): + nbatch = X.shape[0] + nsteps = nbatch // nenv + + h = tf.compat.v1.layers.flatten(X) + + M = tf.compat.v1.placeholder(tf.float32, [nbatch]) # mask (done t-1) + S = tf.compat.v1.placeholder(tf.float32, [nenv, 2 * nlstm]) # states + + xs = batch_to_seq(h, nenv, nsteps) + ms = batch_to_seq(M, nenv, nsteps) + + if layer_norm: + h5, snew = utils.lnlstm(xs, ms, S, scope='lnlstm', nh=nlstm) + else: + h5, snew = utils.lstm(xs, ms, S, scope='lstm', nh=nlstm) + + h = seq_to_batch(h5) + initial_state = np.zeros(S.shape.as_list(), dtype=float) + + return h, {'S': S, 'M': M, 'state': snew, 'initial_state': initial_state} + + return network_fn + + +@register("cnn_lstm") +def cnn_lstm(nlstm=128, layer_norm=False, conv_fn=nature_cnn, **conv_kwargs): + def network_fn(X, nenv=1): + nbatch = X.shape[0] + nsteps = nbatch // nenv + + h = conv_fn(X, **conv_kwargs) + + M = tf.compat.v1.placeholder(tf.float32, [nbatch]) # mask (done t-1) + S = tf.compat.v1.placeholder(tf.float32, [nenv, 2 * nlstm]) # states + + xs = batch_to_seq(h, nenv, nsteps) + ms = batch_to_seq(M, nenv, nsteps) + + if layer_norm: + h5, snew = utils.lnlstm(xs, ms, S, scope='lnlstm', nh=nlstm) + else: + h5, snew = utils.lstm(xs, ms, S, scope='lstm', nh=nlstm) + + h = seq_to_batch(h5) + initial_state = np.zeros(S.shape.as_list(), dtype=float) + + return h, {'S': S, 'M': M, 'state': snew, 'initial_state': initial_state} + + return network_fn + + +@register("impala_cnn_lstm") +def impala_cnn_lstm(): + return cnn_lstm(nlstm=256, conv_fn=build_impala_cnn) + + +@register("cnn_lnlstm") +def cnn_lnlstm(nlstm=128, **conv_kwargs): + return cnn_lstm(nlstm, layer_norm=True, **conv_kwargs) + + +@register("conv_only") +def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs): + ''' + convolutions-only net + + Parameters: + ---------- + + conv: list of triples (filter_number, filter_size, stride) specifying parameters for each layer. + + Returns: + + function that takes tensorflow tensor as input and returns the output of the last convolutional layer + + ''' + + def network_fn(X): + out = tf.cast(X, tf.float32) / 255. + with tf.compat.v1.variable_scope("convnet"): + for num_outputs, kernel_size, stride in convs: + out = tf.contrib.layers.convolution2d(out, + num_outputs=num_outputs, + kernel_size=kernel_size, + stride=stride, + activation_fn=tf.nn.relu, + **conv_kwargs) + + return out + + return network_fn + + +def _normalize_clip_observation(x, clip_range=[-5.0, 5.0]): + rms = RunningMeanStd(shape=x.shape[1:]) + norm_x = tf.clip_by_value((x - rms.mean) / rms.std, min(clip_range), max(clip_range)) + return norm_x, rms + + +def get_network_builder(name): + """ + If you want to register your own network outside models.py, you just need: + + Usage Example: + ------------- + from baselines.common.models import register + @register("your_network_name") + def your_network_define(**net_kwargs): + ... + return network_fn + + """ + if callable(name): + return name + elif name in mapping: + return mapping[name] + else: + raise ValueError('Unknown network type: {}'.format(name)) + + +class VectorQuantizer(layers.Layer): + def __init__(self, num_embeddings, embedding_dim, seed=0, **kwargs): + super().__init__(**kwargs) + self.embedding_dim = embedding_dim + self.num_embeddings = num_embeddings + + # Initialize the embeddings which we will quantize. + w_init = tf.compat.v1.random_uniform_initializer(minval=-1 / num_embeddings, maxval=1 / num_embeddings, + seed=seed) + + self.embeddings = tf.compat.v1.get_variable( + initializer=w_init( + shape=(self.embedding_dim, self.num_embeddings), dtype="float32" + ), + trainable=True, + name="embeddings_vqvae", + ) + + def call(self, x): + # Calculate the input shape of the inputs and + # then flatten the inputs keeping `embedding_dim` intact. + input_shape = tf.shape(input=x) + flattened = tf.reshape(x, [-1, self.embedding_dim]) + + # Quantization. + encoding_indices = self.get_code_indices(flattened) + encoding_indices = tf.reshape(encoding_indices, [input_shape[0], -1]) + encodings = tf.one_hot(encoding_indices, self.num_embeddings) + quantized = tf.matmul(encodings, self.embeddings, transpose_b=True) + quantized = tf.reshape(quantized, input_shape) + + # Straight-through estimator. + quantized_ = x + tf.stop_gradient(quantized - x) + + return quantized_, quantized, encoding_indices + + def get_code_indices(self, flattened_inputs): + # Calculate L2-normalized distance between the inputs and the codes. + similarity = tf.matmul(flattened_inputs, self.embeddings) + distances = ( + tf.reduce_sum(input_tensor=flattened_inputs ** 2, axis=1, keepdims=True) + + tf.reduce_sum(input_tensor=self.embeddings ** 2, axis=0) + - 2 * similarity + ) + + # Derive the indices for minimum distances. + encoding_indices = tf.argmin(input=distances, axis=1) + return encoding_indices diff --git a/baselines/common/mpi_adam.py b/baselines/common/mpi_adam.py new file mode 100644 index 0000000000000000000000000000000000000000..f1103b187a63dd858494a9eadafd73186d33359b --- /dev/null +++ b/baselines/common/mpi_adam.py @@ -0,0 +1,103 @@ +import baselines.common.tf_util as U +import tensorflow as tf +import numpy as np +try: + from mpi4py import MPI +except ImportError: + MPI = None + + +class MpiAdam(object): + def __init__(self, var_list, *, beta1=0.9, beta2=0.999, epsilon=1e-08, scale_grad_by_procs=True, comm=None): + self.var_list = var_list + self.beta1 = beta1 + self.beta2 = beta2 + self.epsilon = epsilon + self.scale_grad_by_procs = scale_grad_by_procs + size = sum(U.numel(v) for v in var_list) + self.m = np.zeros(size, 'float32') + self.v = np.zeros(size, 'float32') + self.t = 0 + self.setfromflat = U.SetFromFlat(var_list) + self.getflat = U.GetFlat(var_list) + self.comm = MPI.COMM_WORLD if comm is None and MPI is not None else comm + + def update(self, localg, stepsize): + if self.t % 100 == 0: + self.check_synced() + localg = localg.astype('float32') + if self.comm is not None: + globalg = np.zeros_like(localg) + self.comm.Allreduce(localg, globalg, op=MPI.SUM) + if self.scale_grad_by_procs: + globalg /= self.comm.Get_size() + else: + globalg = np.copy(localg) + + self.t += 1 + a = stepsize * np.sqrt(1 - self.beta2**self.t)/(1 - self.beta1**self.t) + self.m = self.beta1 * self.m + (1 - self.beta1) * globalg + self.v = self.beta2 * self.v + (1 - self.beta2) * (globalg * globalg) + step = (- a) * self.m / (np.sqrt(self.v) + self.epsilon) + self.setfromflat(self.getflat() + step) + + def sync(self): + if self.comm is None: + return + theta = self.getflat() + self.comm.Bcast(theta, root=0) + self.setfromflat(theta) + + def check_synced(self): + if self.comm is None: + return + if self.comm.Get_rank() == 0: # this is root + theta = self.getflat() + self.comm.Bcast(theta, root=0) + else: + thetalocal = self.getflat() + thetaroot = np.empty_like(thetalocal) + self.comm.Bcast(thetaroot, root=0) + assert (thetaroot == thetalocal).all(), (thetaroot, thetalocal) + +@U.in_session +def test_MpiAdam(): + np.random.seed(0) + tf.compat.v1.set_random_seed(0) + + a = tf.Variable(np.random.randn(3).astype('float32')) + b = tf.Variable(np.random.randn(2,5).astype('float32')) + loss = tf.reduce_sum(input_tensor=tf.square(a)) + tf.reduce_sum(input_tensor=tf.sin(b)) + + stepsize = 1e-2 + update_op = tf.compat.v1.train.AdamOptimizer(stepsize).minimize(loss) + do_update = U.function([], loss, updates=[update_op]) + + tf.compat.v1.get_default_session().run(tf.compat.v1.global_variables_initializer()) + losslist_ref = [] + for i in range(10): + l = do_update() + print(i, l) + losslist_ref.append(l) + + + + tf.compat.v1.set_random_seed(0) + tf.compat.v1.get_default_session().run(tf.compat.v1.global_variables_initializer()) + + var_list = [a,b] + lossandgrad = U.function([], [loss, U.flatgrad(loss, var_list)]) + adam = MpiAdam(var_list) + + losslist_test = [] + for i in range(10): + l,g = lossandgrad() + adam.update(g, stepsize) + print(i,l) + losslist_test.append(l) + + np.testing.assert_allclose(np.array(losslist_ref), np.array(losslist_test), atol=1e-4) + + +if __name__ == '__main__': + test_MpiAdam() diff --git a/baselines/common/mpi_adam_optimizer.py b/baselines/common/mpi_adam_optimizer.py new file mode 100644 index 0000000000000000000000000000000000000000..1fbb1fc4616b79b95178fd7bf2a4a928fd959c62 --- /dev/null +++ b/baselines/common/mpi_adam_optimizer.py @@ -0,0 +1,90 @@ +import numpy as np +import tensorflow as tf +from baselines.common import tf_util as U +from baselines.common.tests.test_with_mpi import with_mpi +from baselines import logger +try: + from mpi4py import MPI +except ImportError: + MPI = None + +class MpiAdamOptimizer(tf.compat.v1.train.AdamOptimizer): + """Adam optimizer that averages gradients across mpi processes.""" + def __init__(self, comm, grad_clip=None, mpi_rank_weight=1, **kwargs): + self.comm = comm + self.grad_clip = grad_clip + self.mpi_rank_weight = mpi_rank_weight + tf.compat.v1.train.AdamOptimizer.__init__(self, **kwargs) + def compute_gradients(self, loss, var_list, **kwargs): + grads_and_vars = tf.compat.v1.train.AdamOptimizer.compute_gradients(self, loss, var_list, **kwargs) + grads_and_vars = [(g, v) for g, v in grads_and_vars if g is not None] + flat_grad = tf.concat([tf.reshape(g, (-1,)) for g, v in grads_and_vars], axis=0) * self.mpi_rank_weight + shapes = [v.shape.as_list() for g, v in grads_and_vars] + sizes = [int(np.prod(s)) for s in shapes] + + total_weight = np.zeros(1, np.float32) + self.comm.Allreduce(np.array([self.mpi_rank_weight], dtype=np.float32), total_weight, op=MPI.SUM) + total_weight = total_weight[0] + + buf = np.zeros(sum(sizes), np.float32) + countholder = [0] # Counts how many times _collect_grads has been called + stat = tf.reduce_sum(input_tensor=grads_and_vars[0][1]) # sum of first variable + def _collect_grads(flat_grad, np_stat): + if self.grad_clip is not None: + gradnorm = np.linalg.norm(flat_grad) + if gradnorm > 1: + flat_grad /= gradnorm + logger.logkv_mean('gradnorm', gradnorm) + logger.logkv_mean('gradclipfrac', float(gradnorm > 1)) + self.comm.Allreduce(flat_grad, buf, op=MPI.SUM) + np.divide(buf, float(total_weight), out=buf) + if countholder[0] % 100 == 0: + check_synced(np_stat, self.comm) + countholder[0] += 1 + return buf + + avg_flat_grad = tf.compat.v1.py_func(_collect_grads, [flat_grad, stat], tf.float32) + avg_flat_grad.set_shape(flat_grad.shape) + avg_grads = tf.split(avg_flat_grad, sizes, axis=0) + avg_grads_and_vars = [(tf.reshape(g, v.shape), v) + for g, (_, v) in zip(avg_grads, grads_and_vars)] + return avg_grads_and_vars + +def check_synced(localval, comm=None): + """ + It's common to forget to initialize your variables to the same values, or + (less commonly) if you update them in some other way than adam, to get them out of sync. + This function checks that variables on all MPI workers are the same, and raises + an AssertionError otherwise + + Arguments: + comm: MPI communicator + localval: list of local variables (list of variables on current worker to be compared with the other workers) + """ + comm = comm or MPI.COMM_WORLD + vals = comm.gather(localval) + if comm.rank == 0: + assert all(val==vals[0] for val in vals[1:]),\ + 'MpiAdamOptimizer detected that different workers have different weights: {}'.format(vals) + +@with_mpi(timeout=5) +def test_nonfreeze(): + np.random.seed(0) + tf.compat.v1.set_random_seed(0) + + a = tf.Variable(np.random.randn(3).astype('float32')) + b = tf.Variable(np.random.randn(2,5).astype('float32')) + loss = tf.reduce_sum(input_tensor=tf.square(a)) + tf.reduce_sum(input_tensor=tf.sin(b)) + + stepsize = 1e-2 + # for some reason the session config with inter_op_parallelism_threads was causing + # nested sess.run calls to freeze + config = tf.compat.v1.ConfigProto(inter_op_parallelism_threads=1) + sess = U.get_session(config=config) + update_op = MpiAdamOptimizer(comm=MPI.COMM_WORLD, learning_rate=stepsize).minimize(loss) + sess.run(tf.compat.v1.global_variables_initializer()) + losslist_ref = [] + for i in range(100): + l,_ = sess.run([loss, update_op]) + print(i, l) + losslist_ref.append(l) diff --git a/baselines/common/mpi_fork.py b/baselines/common/mpi_fork.py new file mode 100644 index 0000000000000000000000000000000000000000..07b555e4fe7bbf121298058a3a89bb30da73a6bd --- /dev/null +++ b/baselines/common/mpi_fork.py @@ -0,0 +1,23 @@ +import os, subprocess, sys + +def mpi_fork(n, bind_to_core=False): + """Re-launches the current script with workers + Returns "parent" for original parent, "child" for MPI children + """ + if n<=1: + return "child" + if os.getenv("IN_MPI") is None: + env = os.environ.copy() + env.update( + MKL_NUM_THREADS="1", + OMP_NUM_THREADS="1", + IN_MPI="1" + ) + args = ["mpirun", "-np", str(n)] + if bind_to_core: + args += ["-bind-to", "core"] + args += [sys.executable] + sys.argv + subprocess.check_call(args, env=env) + return "parent" + else: + return "child" diff --git a/baselines/common/mpi_moments.py b/baselines/common/mpi_moments.py new file mode 100644 index 0000000000000000000000000000000000000000..5510de023480a7db5c27e56f8cb6eb81a40b5c08 --- /dev/null +++ b/baselines/common/mpi_moments.py @@ -0,0 +1,61 @@ +from mpi4py import MPI +import numpy as np +from baselines.common import zipsame + + +def mpi_mean(x, axis=0, comm=None, keepdims=False): + x = np.asarray(x) + assert x.ndim > 0 + if comm is None: comm = MPI.COMM_WORLD + xsum = x.sum(axis=axis, keepdims=keepdims) + n = xsum.size + localsum = np.zeros(n+1, x.dtype) + localsum[:n] = xsum.ravel() + localsum[n] = x.shape[axis] + # globalsum = np.zeros_like(localsum) + # comm.Allreduce(localsum, globalsum, op=MPI.SUM) + globalsum = comm.allreduce(localsum, op=MPI.SUM) + return globalsum[:n].reshape(xsum.shape) / globalsum[n], globalsum[n] + +def mpi_moments(x, axis=0, comm=None, keepdims=False): + x = np.asarray(x) + assert x.ndim > 0 + mean, count = mpi_mean(x, axis=axis, comm=comm, keepdims=True) + sqdiffs = np.square(x - mean) + meansqdiff, count1 = mpi_mean(sqdiffs, axis=axis, comm=comm, keepdims=True) + assert count1 == count + std = np.sqrt(meansqdiff) + if not keepdims: + newshape = mean.shape[:axis] + mean.shape[axis+1:] + mean = mean.reshape(newshape) + std = std.reshape(newshape) + return mean, std, count + + +def test_runningmeanstd(): + import subprocess + subprocess.check_call(['mpirun', '-np', '3', + 'python','-c', + 'from baselines.common.mpi_moments import _helper_runningmeanstd; _helper_runningmeanstd()']) + +def _helper_runningmeanstd(): + comm = MPI.COMM_WORLD + np.random.seed(0) + for (triple,axis) in [ + ((np.random.randn(3), np.random.randn(4), np.random.randn(5)),0), + ((np.random.randn(3,2), np.random.randn(4,2), np.random.randn(5,2)),0), + ((np.random.randn(2,3), np.random.randn(2,4), np.random.randn(2,4)),1), + ]: + + + x = np.concatenate(triple, axis=axis) + ms1 = [x.mean(axis=axis), x.std(axis=axis), x.shape[axis]] + + + ms2 = mpi_moments(triple[comm.Get_rank()],axis=axis) + + for (a1,a2) in zipsame(ms1, ms2): + print(a1, a2) + assert np.allclose(a1, a2) + print("ok!") + diff --git a/baselines/common/mpi_running_mean_std.py b/baselines/common/mpi_running_mean_std.py new file mode 100644 index 0000000000000000000000000000000000000000..ffe4eb31ba5e2c02fa95cc0665b7a09c36618fc5 --- /dev/null +++ b/baselines/common/mpi_running_mean_std.py @@ -0,0 +1,112 @@ +try: + from mpi4py import MPI +except ImportError: + MPI = None + +import tensorflow as tf, baselines.common.tf_util as U, numpy as np + +class RunningMeanStd(object): + # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm + def __init__(self, epsilon=1e-2, shape=()): + + self._sum = tf.compat.v1.get_variable( + dtype=tf.float64, + shape=shape, + initializer=tf.compat.v1.constant_initializer(0.0), + name="runningsum", trainable=False) + self._sumsq = tf.compat.v1.get_variable( + dtype=tf.float64, + shape=shape, + initializer=tf.compat.v1.constant_initializer(epsilon), + name="runningsumsq", trainable=False) + self._count = tf.compat.v1.get_variable( + dtype=tf.float64, + shape=(), + initializer=tf.compat.v1.constant_initializer(epsilon), + name="count", trainable=False) + self.shape = shape + + self.mean = tf.cast(self._sum / self._count, dtype=tf.float32) + self.std = tf.sqrt( tf.maximum( tf.cast(self._sumsq / self._count, dtype=tf.float32) - tf.square(self.mean) , 1e-2 )) + + newsum = tf.compat.v1.placeholder(shape=self.shape, dtype=tf.float64, name='sum') + newsumsq = tf.compat.v1.placeholder(shape=self.shape, dtype=tf.float64, name='var') + newcount = tf.compat.v1.placeholder(shape=[], dtype=tf.float64, name='count') + self.incfiltparams = U.function([newsum, newsumsq, newcount], [], + updates=[tf.compat.v1.assign_add(self._sum, newsum), + tf.compat.v1.assign_add(self._sumsq, newsumsq), + tf.compat.v1.assign_add(self._count, newcount)]) + + + def update(self, x): + x = x.astype('float64') + n = int(np.prod(self.shape)) + totalvec = np.zeros(n*2+1, 'float64') + addvec = np.concatenate([x.sum(axis=0).ravel(), np.square(x).sum(axis=0).ravel(), np.array([len(x)],dtype='float64')]) + if MPI is not None: + MPI.COMM_WORLD.Allreduce(addvec, totalvec, op=MPI.SUM) + self.incfiltparams(totalvec[0:n].reshape(self.shape), totalvec[n:2*n].reshape(self.shape), totalvec[2*n]) + +@U.in_session +def test_runningmeanstd(): + for (x1, x2, x3) in [ + (np.random.randn(3), np.random.randn(4), np.random.randn(5)), + (np.random.randn(3,2), np.random.randn(4,2), np.random.randn(5,2)), + ]: + + rms = RunningMeanStd(epsilon=0.0, shape=x1.shape[1:]) + U.initialize() + + x = np.concatenate([x1, x2, x3], axis=0) + ms1 = [x.mean(axis=0), x.std(axis=0)] + rms.update(x1) + rms.update(x2) + rms.update(x3) + ms2 = [rms.mean.eval(), rms.std.eval()] + + assert np.allclose(ms1, ms2) + +@U.in_session +def test_dist(): + np.random.seed(0) + p1,p2,p3=(np.random.randn(3,1), np.random.randn(4,1), np.random.randn(5,1)) + q1,q2,q3=(np.random.randn(6,1), np.random.randn(7,1), np.random.randn(8,1)) + + # p1,p2,p3=(np.random.randn(3), np.random.randn(4), np.random.randn(5)) + # q1,q2,q3=(np.random.randn(6), np.random.randn(7), np.random.randn(8)) + + comm = MPI.COMM_WORLD + assert comm.Get_size()==2 + if comm.Get_rank()==0: + x1,x2,x3 = p1,p2,p3 + elif comm.Get_rank()==1: + x1,x2,x3 = q1,q2,q3 + else: + assert False + + rms = RunningMeanStd(epsilon=0.0, shape=(1,)) + U.initialize() + + rms.update(x1) + rms.update(x2) + rms.update(x3) + + bigvec = np.concatenate([p1,p2,p3,q1,q2,q3]) + + def checkallclose(x,y): + print(x,y) + return np.allclose(x,y) + + assert checkallclose( + bigvec.mean(axis=0), + rms.mean.eval(), + ) + assert checkallclose( + bigvec.std(axis=0), + rms.std.eval(), + ) + + +if __name__ == "__main__": + # Run with mpirun -np 2 python + test_dist() diff --git a/baselines/common/mpi_util.py b/baselines/common/mpi_util.py new file mode 100644 index 0000000000000000000000000000000000000000..2b0c810ffc846453dd80c0ff413368e982b80a20 --- /dev/null +++ b/baselines/common/mpi_util.py @@ -0,0 +1,133 @@ +from collections import defaultdict +import os, numpy as np +import platform +import shutil +import subprocess +import warnings +import sys + +try: + from mpi4py import MPI +except ImportError: + MPI = None + + +def sync_from_root(sess, variables, comm=None): + """ + Send the root node's parameters to every worker. + Arguments: + sess: the TensorFlow session. + variables: all parameter variables including optimizer's + """ + if comm is None: comm = MPI.COMM_WORLD + import tensorflow as tf + values = comm.bcast(sess.run(variables)) + sess.run([tf.compat.v1.assign(var, val) + for (var, val) in zip(variables, values)]) + +def gpu_count(): + """ + Count the GPUs on this machine. + """ + if shutil.which('nvidia-smi') is None: + return 0 + output = subprocess.check_output(['nvidia-smi', '--query-gpu=gpu_name', '--format=csv']) + return max(0, len(output.split(b'\n')) - 2) + +def setup_mpi_gpus(): + """ + Set CUDA_VISIBLE_DEVICES to MPI rank if not already set + """ + if 'CUDA_VISIBLE_DEVICES' not in os.environ: + if sys.platform == 'darwin': # This Assumes if you're on OSX you're just + ids = [] # doing a smoke test and don't want GPUs + else: + lrank, _lsize = get_local_rank_size(MPI.COMM_WORLD) + ids = [lrank] + os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, ids)) + +def get_local_rank_size(comm): + """ + Returns the rank of each process on its machine + The processes on a given machine will be assigned ranks + 0, 1, 2, ..., N-1, + where N is the number of processes on this machine. + + Useful if you want to assign one gpu per machine + """ + this_node = platform.node() + ranks_nodes = comm.allgather((comm.Get_rank(), this_node)) + node2rankssofar = defaultdict(int) + local_rank = None + for (rank, node) in ranks_nodes: + if rank == comm.Get_rank(): + local_rank = node2rankssofar[node] + node2rankssofar[node] += 1 + assert local_rank is not None + return local_rank, node2rankssofar[this_node] + +def share_file(comm, path): + """ + Copies the file from rank 0 to all other ranks + Puts it in the same place on all machines + """ + localrank, _ = get_local_rank_size(comm) + if comm.Get_rank() == 0: + with open(path, 'rb') as fh: + data = fh.read() + comm.bcast(data) + else: + data = comm.bcast(None) + if localrank == 0: + os.makedirs(os.path.dirname(path), exist_ok=True) + with open(path, 'wb') as fh: + fh.write(data) + comm.Barrier() + +def dict_gather(comm, d, op='mean', assert_all_have_data=True): + """ + Perform a reduction operation over dicts + """ + if comm is None: return d + alldicts = comm.allgather(d) + size = comm.size + k2li = defaultdict(list) + for d in alldicts: + for (k,v) in d.items(): + k2li[k].append(v) + result = {} + for (k,li) in k2li.items(): + if assert_all_have_data: + assert len(li)==size, "only %i out of %i MPI workers have sent '%s'" % (len(li), size, k) + if op=='mean': + result[k] = np.mean(li, axis=0) + elif op=='sum': + result[k] = np.sum(li, axis=0) + else: + assert 0, op + return result + +def mpi_weighted_mean(comm, local_name2valcount): + """ + Perform a weighted average over dicts that are each on a different node + Input: local_name2valcount: dict mapping key -> (value, count) + Returns: key -> mean + """ + all_name2valcount = comm.gather(local_name2valcount) + if comm.rank == 0: + name2sum = defaultdict(float) + name2count = defaultdict(float) + for n2vc in all_name2valcount: + for (name, (val, count)) in n2vc.items(): + try: + val = float(val) + except ValueError: + if comm.rank == 0: + warnings.warn('WARNING: tried to compute mean on non-float {}={}'.format(name, val)) + else: + name2sum[name] += val * count + name2count[name] += count + return {name : name2sum[name] / name2count[name] for name in name2sum} + else: + return {} + diff --git a/baselines/common/plot_util.py b/baselines/common/plot_util.py new file mode 100644 index 0000000000000000000000000000000000000000..e15c5082a4d3bd117d037771b93ab466af2aca0d --- /dev/null +++ b/baselines/common/plot_util.py @@ -0,0 +1,434 @@ +import matplotlib.pyplot as plt +import os.path as osp +import json +import os +import numpy as np +import pandas +from collections import defaultdict, namedtuple +from baselines.bench import monitor +from baselines.logger import read_json, read_csv + +def smooth(y, radius, mode='two_sided', valid_only=False): + ''' + Smooth signal y, where radius is determines the size of the window + + mode='twosided': + average over the window [max(index - radius, 0), min(index + radius, len(y)-1)] + mode='causal': + average over the window [max(index - radius, 0), index] + + valid_only: put nan in entries where the full-sized window is not available + + ''' + assert mode in ('two_sided', 'causal') + if len(y) < 2*radius+1: + return np.ones_like(y) * y.mean() + elif mode == 'two_sided': + convkernel = np.ones(2 * radius+1) + out = np.convolve(y, convkernel,mode='same') / np.convolve(np.ones_like(y), convkernel, mode='same') + if valid_only: + out[:radius] = out[-radius:] = np.nan + elif mode == 'causal': + convkernel = np.ones(radius) + out = np.convolve(y, convkernel,mode='full') / np.convolve(np.ones_like(y), convkernel, mode='full') + out = out[:-radius+1] + if valid_only: + out[:radius] = np.nan + return out + +def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8): + ''' + perform one-sided (causal) EMA (exponential moving average) + smoothing and resampling to an even grid with n points. + Does not do extrapolation, so we assume + xolds[0] <= low && high <= xolds[-1] + + Arguments: + + xolds: array or list - x values of data. Needs to be sorted in ascending order + yolds: array of list - y values of data. Has to have the same length as xolds + + low: float - min value of the new x grid. By default equals to xolds[0] + high: float - max value of the new x grid. By default equals to xolds[-1] + + n: int - number of points in new x grid + + decay_steps: float - EMA decay factor, expressed in new x grid steps. + + low_counts_threshold: float or int + - y values with counts less than this value will be set to NaN + + Returns: + tuple sum_ys, count_ys where + xs - array with new x grid + ys - array of EMA of y at each point of the new x grid + count_ys - array of EMA of y counts at each point of the new x grid + + ''' + + low = xolds[0] if low is None else low + high = xolds[-1] if high is None else high + + assert xolds[0] <= low, 'low = {} < xolds[0] = {} - extrapolation not permitted!'.format(low, xolds[0]) + assert xolds[-1] >= high, 'high = {} > xolds[-1] = {} - extrapolation not permitted!'.format(high, xolds[-1]) + assert len(xolds) == len(yolds), 'length of xolds ({}) and yolds ({}) do not match!'.format(len(xolds), len(yolds)) + + + xolds = xolds.astype('float64') + yolds = yolds.astype('float64') + + luoi = 0 # last unused old index + sum_y = 0. + count_y = 0. + xnews = np.linspace(low, high, n) + decay_period = (high - low) / (n - 1) * decay_steps + interstep_decay = np.exp(- 1. / decay_steps) + sum_ys = np.zeros_like(xnews) + count_ys = np.zeros_like(xnews) + for i in range(n): + xnew = xnews[i] + sum_y *= interstep_decay + count_y *= interstep_decay + while True: + if luoi >= len(xolds): + break + xold = xolds[luoi] + if xold <= xnew: + decay = np.exp(- (xnew - xold) / decay_period) + sum_y += decay * yolds[luoi] + count_y += decay + luoi += 1 + else: + break + sum_ys[i] = sum_y + count_ys[i] = count_y + + ys = sum_ys / count_ys + ys[count_ys < low_counts_threshold] = np.nan + + return xnews, ys, count_ys + +def symmetric_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8): + ''' + perform symmetric EMA (exponential moving average) + smoothing and resampling to an even grid with n points. + Does not do extrapolation, so we assume + xolds[0] <= low && high <= xolds[-1] + + Arguments: + + xolds: array or list - x values of data. Needs to be sorted in ascending order + yolds: array of list - y values of data. Has to have the same length as xolds + + low: float - min value of the new x grid. By default equals to xolds[0] + high: float - max value of the new x grid. By default equals to xolds[-1] + + n: int - number of points in new x grid + + decay_steps: float - EMA decay factor, expressed in new x grid steps. + + low_counts_threshold: float or int + - y values with counts less than this value will be set to NaN + + Returns: + tuple sum_ys, count_ys where + xs - array with new x grid + ys - array of EMA of y at each point of the new x grid + count_ys - array of EMA of y counts at each point of the new x grid + + ''' + xs, ys1, count_ys1 = one_sided_ema(xolds, yolds, low, high, n, decay_steps, low_counts_threshold=0) + _, ys2, count_ys2 = one_sided_ema(-xolds[::-1], yolds[::-1], -high, -low, n, decay_steps, low_counts_threshold=0) + ys2 = ys2[::-1] + count_ys2 = count_ys2[::-1] + count_ys = count_ys1 + count_ys2 + ys = (ys1 * count_ys1 + ys2 * count_ys2) / count_ys + ys[count_ys < low_counts_threshold] = np.nan + return xs, ys, count_ys + +Result = namedtuple('Result', 'monitor progress dirname metadata') +Result.__new__.__defaults__ = (None,) * len(Result._fields) + +def load_results(root_dir_or_dirs, enable_progress=True, enable_monitor=True, verbose=False): + ''' + load summaries of runs from a list of directories (including subdirectories) + Arguments: + + enable_progress: bool - if True, will attempt to load data from progress.csv files (data saved by logger). Default: True + + enable_monitor: bool - if True, will attempt to load data from monitor.csv files (data saved by Monitor environment wrapper). Default: True + + verbose: bool - if True, will print out list of directories from which the data is loaded. Default: False + + + Returns: + List of Result objects with the following fields: + - dirname - path to the directory data was loaded from + - metadata - run metadata (such as command-line arguments and anything else in metadata.json file + - monitor - if enable_monitor is True, this field contains pandas dataframe with loaded monitor.csv file (or aggregate of all *.monitor.csv files in the directory) + - progress - if enable_progress is True, this field contains pandas dataframe with loaded progress.csv file + ''' + import re + if isinstance(root_dir_or_dirs, str): + rootdirs = [osp.expanduser(root_dir_or_dirs)] + else: + rootdirs = [osp.expanduser(d) for d in root_dir_or_dirs] + allresults = [] + for rootdir in rootdirs: + assert osp.exists(rootdir), "%s doesn't exist"%rootdir + for dirname, dirs, files in os.walk(rootdir): + if '-proc' in dirname: + files[:] = [] + continue + monitor_re = re.compile(r'(\d+\.)?(\d+\.)?monitor\.csv') + if set(['metadata.json', 'monitor.json', 'progress.json', 'progress.csv']).intersection(files) or \ + any([f for f in files if monitor_re.match(f)]): # also match monitor files like 0.1.monitor.csv + # used to be uncommented, which means do not go deeper than current directory if any of the data files + # are found + # dirs[:] = [] + result = {'dirname' : dirname} + if "metadata.json" in files: + with open(osp.join(dirname, "metadata.json"), "r") as fh: + result['metadata'] = json.load(fh) + progjson = osp.join(dirname, "progress.json") + progcsv = osp.join(dirname, "progress.csv") + if enable_progress: + if osp.exists(progjson): + result['progress'] = pandas.DataFrame(read_json(progjson)) + elif osp.exists(progcsv): + try: + result['progress'] = read_csv(progcsv) + except pandas.errors.EmptyDataError: + print('skipping progress file in ', dirname, 'empty data') + else: + if verbose: print('skipping %s: no progress file'%dirname) + + if enable_monitor: + try: + result['monitor'] = pandas.DataFrame(monitor.load_results(dirname)) + except monitor.LoadMonitorResultsError: + print('skipping %s: no monitor files'%dirname) + except Exception as e: + print('exception loading monitor file in %s: %s'%(dirname, e)) + + if result.get('monitor') is not None or result.get('progress') is not None: + allresults.append(Result(**result)) + if verbose: + print('successfully loaded %s'%dirname) + + if verbose: print('loaded %i results'%len(allresults)) + return allresults + +COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink', + 'brown', 'orange', 'teal', 'lightblue', 'lime', 'lavender', 'turquoise', + 'darkgreen', 'tan', 'salmon', 'gold', 'darkred', 'darkblue'] + + +def default_xy_fn(r): + x = np.cumsum(r.monitor.l) + y = smooth(r.monitor.r, radius=10) + return x,y + +def default_split_fn(r): + import re + # match name between slash and - at the end of the string + # (slash in the beginning or - in the end or either may be missing) + match = re.search(r'[^/-]+(?=(-\d+)?\Z)', r.dirname) + if match: + return match.group(0) + +def plot_results( + allresults, *, + xy_fn=default_xy_fn, + split_fn=default_split_fn, + group_fn=default_split_fn, + average_group=False, + shaded_std=True, + shaded_err=True, + figsize=None, + legend_outside=False, + resample=0, + smooth_step=1.0, + tiling='vertical', + xlabel=None, + ylabel=None +): + ''' + Plot multiple Results objects + + xy_fn: function Result -> x,y - function that converts results objects into tuple of x and y values. + By default, x is cumsum of episode lengths, and y is episode rewards + + split_fn: function Result -> hashable - function that converts results objects into keys to split curves into sub-panels by. + That is, the results r for which split_fn(r) is different will be put on different sub-panels. + By default, the portion of r.dirname between last / and - is returned. The sub-panels are + stacked vertically in the figure. + + group_fn: function Result -> hashable - function that converts results objects into keys to group curves by. + That is, the results r for which group_fn(r) is the same will be put into the same group. + Curves in the same group have the same color (if average_group is False), or averaged over + (if average_group is True). The default value is the same as default value for split_fn + + average_group: bool - if True, will average the curves in the same group and plot the mean. Enables resampling + (if resample = 0, will use 512 steps) + + shaded_std: bool - if True (default), the shaded region corresponding to standard deviation of the group of curves will be + shown (only applicable if average_group = True) + + shaded_err: bool - if True (default), the shaded region corresponding to error in mean estimate of the group of curves + (that is, standard deviation divided by square root of number of curves) will be + shown (only applicable if average_group = True) + + figsize: tuple or None - size of the resulting figure (including sub-panels). By default, width is 6 and height is 6 times number of + sub-panels. + + + legend_outside: bool - if True, will place the legend outside of the sub-panels. + + resample: int - if not zero, size of the uniform grid in x direction to resample onto. Resampling is performed via symmetric + EMA smoothing (see the docstring for symmetric_ema). + Default is zero (no resampling). Note that if average_group is True, resampling is necessary; in that case, default + value is 512. + + smooth_step: float - when resampling (i.e. when resample > 0 or average_group is True), use this EMA decay parameter (in units of the new grid step). + See docstrings for decay_steps in symmetric_ema or one_sided_ema functions. + + ''' + + if split_fn is None: split_fn = lambda _ : '' + if group_fn is None: group_fn = lambda _ : '' + sk2r = defaultdict(list) # splitkey2results + for result in allresults: + splitkey = split_fn(result) + sk2r[splitkey].append(result) + assert len(sk2r) > 0 + assert isinstance(resample, int), "0: don't resample. : that many samples" + if tiling == 'vertical' or tiling is None: + nrows = len(sk2r) + ncols = 1 + elif tiling == 'horizontal': + ncols = len(sk2r) + nrows = 1 + elif tiling == 'symmetric': + import math + N = len(sk2r) + largest_divisor = 1 + for i in range(1, int(math.sqrt(N))+1): + if N % i == 0: + largest_divisor = i + ncols = largest_divisor + nrows = N // ncols + figsize = figsize or (6 * ncols, 6 * nrows) + + f, axarr = plt.subplots(nrows, ncols, sharex=False, squeeze=False, figsize=figsize) + + groups = list(set(group_fn(result) for result in allresults)) + + default_samples = 512 + if average_group: + resample = resample or default_samples + + for (isplit, sk) in enumerate(sorted(sk2r.keys())): + g2l = {} + g2c = defaultdict(int) + sresults = sk2r[sk] + gresults = defaultdict(list) + idx_row = isplit // ncols + idx_col = isplit % ncols + ax = axarr[idx_row][idx_col] + for result in sresults: + group = group_fn(result) + g2c[group] += 1 + x, y = xy_fn(result) + if x is None: x = np.arange(len(y)) + x, y = map(np.asarray, (x, y)) + if average_group: + gresults[group].append((x,y)) + else: + if resample: + x, y, counts = symmetric_ema(x, y, x[0], x[-1], resample, decay_steps=smooth_step) + l, = ax.plot(x, y, color=COLORS[groups.index(group) % len(COLORS)]) + g2l[group] = l + if average_group: + for group in sorted(groups): + xys = gresults[group] + if not any(xys): + continue + color = COLORS[groups.index(group) % len(COLORS)] + origxs = [xy[0] for xy in xys] + minxlen = min(map(len, origxs)) + def allequal(qs): + return all((q==qs[0]).all() for q in qs[1:]) + if resample: + low = max(x[0] for x in origxs) + high = min(x[-1] for x in origxs) + usex = np.linspace(low, high, resample) + ys = [] + for (x, y) in xys: + ys.append(symmetric_ema(x, y, low, high, resample, decay_steps=smooth_step)[1]) + else: + assert allequal([x[:minxlen] for x in origxs]),\ + 'If you want to average unevenly sampled data, set resample=' + usex = origxs[0] + ys = [xy[1][:minxlen] for xy in xys] + ymean = np.mean(ys, axis=0) + ystd = np.std(ys, axis=0) + ystderr = ystd / np.sqrt(len(ys)) + l, = axarr[idx_row][idx_col].plot(usex, ymean, color=color) + g2l[group] = l + if shaded_err: + ax.fill_between(usex, ymean - ystderr, ymean + ystderr, color=color, alpha=.4) + if shaded_std: + ax.fill_between(usex, ymean - ystd, ymean + ystd, color=color, alpha=.2) + + + # https://matplotlib.org/users/legend_guide.html + plt.tight_layout() + if any(g2l.keys()): + ax.legend( + g2l.values(), + ['%s (%i)'%(g, g2c[g]) for g in g2l] if average_group else g2l.keys(), + loc=2 if legend_outside else None, + bbox_to_anchor=(1,1) if legend_outside else None) + ax.set_title(sk) + # add xlabels, but only to the bottom row + if xlabel is not None: + for ax in axarr[-1]: + plt.sca(ax) + plt.xlabel(xlabel) + # add ylabels, but only to left column + if ylabel is not None: + for ax in axarr[:,0]: + plt.sca(ax) + plt.ylabel(ylabel) + + return f, axarr + +def regression_analysis(df): + xcols = list(df.columns.copy()) + xcols.remove('score') + ycols = ['score'] + import statsmodels.api as sm + mod = sm.OLS(df[ycols], sm.add_constant(df[xcols]), hasconst=False) + res = mod.fit() + print(res.summary()) + +def test_smooth(): + norig = 100 + nup = 300 + ndown = 30 + xs = np.cumsum(np.random.rand(norig) * 10 / norig) + yclean = np.sin(xs) + ys = yclean + .1 * np.random.randn(yclean.size) + xup, yup, _ = symmetric_ema(xs, ys, xs.min(), xs.max(), nup, decay_steps=nup/ndown) + xdown, ydown, _ = symmetric_ema(xs, ys, xs.min(), xs.max(), ndown, decay_steps=ndown/ndown) + xsame, ysame, _ = symmetric_ema(xs, ys, xs.min(), xs.max(), norig, decay_steps=norig/ndown) + plt.plot(xs, ys, label='orig', marker='x') + plt.plot(xup, yup, label='up', marker='x') + plt.plot(xdown, ydown, label='down', marker='x') + plt.plot(xsame, ysame, label='same', marker='x') + plt.plot(xs, yclean, label='clean', marker='x') + plt.legend() + plt.show() + + diff --git a/baselines/common/policies.py b/baselines/common/policies.py new file mode 100644 index 0000000000000000000000000000000000000000..551c78b94d2af4ed99c47fb78c7e6d9f93ef9531 --- /dev/null +++ b/baselines/common/policies.py @@ -0,0 +1,186 @@ +import tensorflow as tf +from baselines.common import tf_util +from baselines.a2c.utils import fc +from baselines.common.distributions import make_pdtype +from baselines.common.input import observation_placeholder, encode_observation +from baselines.common.tf_util import adjust_shape +from baselines.common.mpi_running_mean_std import RunningMeanStd +from baselines.common.models import get_network_builder + +import gym + + +class PolicyWithValue(object): + """ + Encapsulates fields and methods for RL policy and value function estimation with shared parameters + """ + + def __init__(self, env, observations, latent, estimate_q=False, vf_latent=None, sess=None, **tensors): + """ + Parameters: + ---------- + env RL environment + + observations tensorflow placeholder in which the observations will be fed + + latent latent state from which policy distribution parameters should be inferred + + vf_latent latent state from which value function should be inferred (if None, then latent is used) + + sess tensorflow session to run calculations in (if None, default session is used) + + **tensors tensorflow tensors for additional attributes such as state or mask + + """ + + self.X = observations + self.state = tf.constant([]) + self.initial_state = None + self.__dict__.update(tensors) + + vf_latent = vf_latent if vf_latent is not None else latent + + vf_latent = tf.compat.v1.layers.flatten(vf_latent) + latent = tf.compat.v1.layers.flatten(latent) + + # Based on the action space, will select what probability distribution type + self.pdtype = make_pdtype(env.action_space) + + self.pd, self.pi = self.pdtype.pdfromlatent(latent, init_scale=0.01) + + # Take an action + self.action = self.pd.sample() + + # Calculate the neg log of our probability + self.neglogp = self.pd.neglogp(self.action) + self.sess = sess or tf.compat.v1.get_default_session() + + if estimate_q: + assert isinstance(env.action_space, gym.spaces.Discrete) + self.q = fc(vf_latent, 'q', env.action_space.n) + self.vf = self.q + else: + self.vf = fc(vf_latent, 'vf', 1) + self.vf = self.vf[:,0] + + def _evaluate(self, variables, observation, **extra_feed): + sess = self.sess + feed_dict = {self.X: adjust_shape(self.X, observation)} + for inpt_name, data in extra_feed.items(): + if inpt_name in self.__dict__.keys(): + inpt = self.__dict__[inpt_name] + if isinstance(inpt, tf.Tensor) and inpt._op.type == 'Placeholder': + feed_dict[inpt] = adjust_shape(inpt, data) + + return sess.run(variables, feed_dict) + + def step(self, observation, **extra_feed): + """ + Compute next action(s) given the observation(s) + + Parameters: + ---------- + + observation observation data (either single or a batch) + + **extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__) + + Returns: + ------- + (action, value estimate, next state, negative log likelihood of the action under current policy parameters) tuple + """ + + a, v, state, neglogp = self._evaluate([self.action, self.vf, self.state, self.neglogp], observation, **extra_feed) + if state.size == 0: + state = None + return a, v, state, neglogp + + def value(self, ob, *args, **kwargs): + """ + Compute value estimate(s) given the observation(s) + + Parameters: + ---------- + + observation observation data (either single or a batch) + + **extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__) + + Returns: + ------- + value estimate + """ + return self._evaluate(self.vf, ob, *args, **kwargs) + + def save(self, save_path): + tf_util.save_state(save_path, sess=self.sess) + + def load(self, load_path): + tf_util.load_state(load_path, sess=self.sess) + +def build_policy(env, policy_network, value_network=None, normalize_observations=False, estimate_q=False, **policy_kwargs): + if isinstance(policy_network, str): + network_type = policy_network + policy_network = get_network_builder(network_type)(**policy_kwargs) + + def policy_fn(nbatch=None, nsteps=None, sess=None, observ_placeholder=None): + ob_space = env.observation_space + + X = observ_placeholder if observ_placeholder is not None else observation_placeholder(ob_space, batch_size=nbatch) + + extra_tensors = {} + + if normalize_observations and X.dtype == tf.float32: + encoded_x, rms = _normalize_clip_observation(X) + extra_tensors['rms'] = rms + else: + encoded_x = X + + encoded_x = encode_observation(ob_space, encoded_x) + + with tf.compat.v1.variable_scope('pi', reuse=tf.compat.v1.AUTO_REUSE): + policy_latent = policy_network(encoded_x) + if isinstance(policy_latent, tuple): + policy_latent, recurrent_tensors = policy_latent + + if recurrent_tensors is not None: + # recurrent architecture, need a few more steps + nenv = nbatch // nsteps + assert nenv > 0, 'Bad input for recurrent policy: batch size {} smaller than nsteps {}'.format(nbatch, nsteps) + policy_latent, recurrent_tensors = policy_network(encoded_x, nenv) + extra_tensors.update(recurrent_tensors) + + + _v_net = value_network + + if _v_net is None or _v_net == 'shared': + vf_latent = policy_latent + else: + if _v_net == 'copy': + _v_net = policy_network + else: + assert callable(_v_net) + + with tf.compat.v1.variable_scope('vf', reuse=tf.compat.v1.AUTO_REUSE): + # TODO recurrent architectures are not supported with value_network=copy yet + vf_latent = _v_net(encoded_x) + + policy = PolicyWithValue( + env=env, + observations=X, + latent=policy_latent, + vf_latent=vf_latent, + sess=sess, + estimate_q=estimate_q, + **extra_tensors + ) + return policy + + return policy_fn + + +def _normalize_clip_observation(x, clip_range=[-5.0, 5.0]): + rms = RunningMeanStd(shape=x.shape[1:]) + norm_x = tf.clip_by_value((x - rms.mean) / rms.std, min(clip_range), max(clip_range)) + return norm_x, rms + diff --git a/baselines/common/retro_wrappers.py b/baselines/common/retro_wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..badbbdd634aa6aedcbba990f32654ea961394c46 --- /dev/null +++ b/baselines/common/retro_wrappers.py @@ -0,0 +1,280 @@ +from collections import deque +import cv2 +cv2.ocl.setUseOpenCL(False) +from .atari_wrappers import WarpFrame, ClipRewardEnv, FrameStack, ScaledFloatFrame +from .wrappers import TimeLimit +import numpy as np +import gym + + +class StochasticFrameSkip(gym.Wrapper): + def __init__(self, env, n, stickprob): + gym.Wrapper.__init__(self, env) + self.n = n + self.stickprob = stickprob + self.curac = None + self.rng = np.random.RandomState() + self.supports_want_render = hasattr(env, "supports_want_render") + + def reset(self, **kwargs): + self.curac = None + return self.env.reset(**kwargs) + + def step(self, ac): + done = False + totrew = 0 + for i in range(self.n): + # First step after reset, use action + if self.curac is None: + self.curac = ac + # First substep, delay with probability=stickprob + elif i==0: + if self.rng.rand() > self.stickprob: + self.curac = ac + # Second substep, new action definitely kicks in + elif i==1: + self.curac = ac + if self.supports_want_render and i self.channel + for _ in range(self.k): + self.frames.append(ob) + return self._get_ob() + + def step(self, ac): + ob, reward, done, info = self.env.step(ac) + self.frames.append(ob) + return self._get_ob(), reward, done, info + + def _get_ob(self): + assert len(self.frames) == self.k + return np.concatenate([frame if i==self.k-1 else frame[:,:,self.channel:self.channel+1] + for (i, frame) in enumerate(self.frames)], axis=2) + +class Downsample(gym.ObservationWrapper): + def __init__(self, env, ratio): + """ + Downsample images by a factor of ratio + """ + gym.ObservationWrapper.__init__(self, env) + (oldh, oldw, oldc) = env.observation_space.shape + newshape = (oldh//ratio, oldw//ratio, oldc) + self.observation_space = gym.spaces.Box(low=0, high=255, + shape=newshape, dtype=np.uint8) + + def observation(self, frame): + height, width, _ = self.observation_space.shape + frame = cv2.resize(frame, (width, height), interpolation=cv2.INTER_AREA) + if frame.ndim == 2: + frame = frame[:,:,None] + return frame + +class Rgb2gray(gym.ObservationWrapper): + def __init__(self, env): + """ + Downsample images by a factor of ratio + """ + gym.ObservationWrapper.__init__(self, env) + (oldh, oldw, _oldc) = env.observation_space.shape + self.observation_space = gym.spaces.Box(low=0, high=255, + shape=(oldh, oldw, 1), dtype=np.uint8) + + def observation(self, frame): + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) + return frame[:,:,None] + + +class MovieRecord(gym.Wrapper): + def __init__(self, env, savedir, k): + gym.Wrapper.__init__(self, env) + self.savedir = savedir + self.k = k + self.epcount = 0 + def reset(self): + if self.epcount % self.k == 0: + self.env.unwrapped.movie_path = self.savedir + else: + self.env.unwrapped.movie_path = None + self.env.unwrapped.movie = None + self.epcount += 1 + return self.env.reset() + +class AppendTimeout(gym.Wrapper): + def __init__(self, env): + gym.Wrapper.__init__(self, env) + self.action_space = env.action_space + self.timeout_space = gym.spaces.Box(low=np.array([0.0]), high=np.array([1.0]), dtype=np.float32) + self.original_os = env.observation_space + if isinstance(self.original_os, gym.spaces.Dict): + import copy + ordered_dict = copy.deepcopy(self.original_os.spaces) + ordered_dict['value_estimation_timeout'] = self.timeout_space + self.observation_space = gym.spaces.Dict(ordered_dict) + self.dict_mode = True + else: + self.observation_space = gym.spaces.Dict({ + 'original': self.original_os, + 'value_estimation_timeout': self.timeout_space + }) + self.dict_mode = False + self.ac_count = None + while 1: + if not hasattr(env, "_max_episode_steps"): # Looking for TimeLimit wrapper that has this field + env = env.env + continue + break + self.timeout = env._max_episode_steps + + def step(self, ac): + self.ac_count += 1 + ob, rew, done, info = self.env.step(ac) + return self._process(ob), rew, done, info + + def reset(self): + self.ac_count = 0 + return self._process(self.env.reset()) + + def _process(self, ob): + fracmissing = 1 - self.ac_count / self.timeout + if self.dict_mode: + ob['value_estimation_timeout'] = fracmissing + else: + return { 'original': ob, 'value_estimation_timeout': fracmissing } + +class StartDoingRandomActionsWrapper(gym.Wrapper): + """ + Warning: can eat info dicts, not good if you depend on them + """ + def __init__(self, env, max_random_steps, on_startup=True, every_episode=False): + gym.Wrapper.__init__(self, env) + self.on_startup = on_startup + self.every_episode = every_episode + self.random_steps = max_random_steps + self.last_obs = None + if on_startup: + self.some_random_steps() + + def some_random_steps(self): + self.last_obs = self.env.reset() + n = np.random.randint(self.random_steps) + #print("running for random %i frames" % n) + for _ in range(n): + self.last_obs, _, done, _ = self.env.step(self.env.action_space.sample()) + if done: self.last_obs = self.env.reset() + + def reset(self): + return self.last_obs + + def step(self, a): + self.last_obs, rew, done, info = self.env.step(a) + if done: + self.last_obs = self.env.reset() + if self.every_episode: + self.some_random_steps() + return self.last_obs, rew, done, info + +def make_retro(*, game, state=None, max_episode_steps=4500, **kwargs): + import retro + if state is None: + state = retro.State.DEFAULT + env = retro.make(game, state, **kwargs) + env = StochasticFrameSkip(env, n=4, stickprob=0.25) + if max_episode_steps is not None: + env = TimeLimit(env, max_episode_steps=max_episode_steps) + return env + +def wrap_deepmind_retro(env, scale=True, frame_stack=4): + """ + Configure environment for retro games, using config similar to DeepMind-style Atari in wrap_deepmind + """ + env = WarpFrame(env) + env = ClipRewardEnv(env) + if frame_stack > 1: + env = FrameStack(env, frame_stack) + if scale: + env = ScaledFloatFrame(env) + return env + +class SonicDiscretizer(gym.ActionWrapper): + """ + Wrap a gym-retro environment and make it use discrete + actions for the Sonic game. + """ + def __init__(self, env): + super(SonicDiscretizer, self).__init__(env) + buttons = ["B", "A", "MODE", "START", "UP", "DOWN", "LEFT", "RIGHT", "C", "Y", "X", "Z"] + actions = [['LEFT'], ['RIGHT'], ['LEFT', 'DOWN'], ['RIGHT', 'DOWN'], ['DOWN'], + ['DOWN', 'B'], ['B']] + self._actions = [] + for action in actions: + arr = np.array([False] * 12) + for button in action: + arr[buttons.index(button)] = True + self._actions.append(arr) + self.action_space = gym.spaces.Discrete(len(self._actions)) + + def action(self, a): # pylint: disable=W0221 + return self._actions[a].copy() + +class RewardScaler(gym.RewardWrapper): + """ + Bring rewards to a reasonable scale for PPO. + This is incredibly important and effects performance + drastically. + """ + def __init__(self, env, scale=0.01): + super(RewardScaler, self).__init__(env) + self.scale = scale + + def reward(self, reward): + return reward * self.scale + +class AllowBacktracking(gym.Wrapper): + """ + Use deltas in max(X) as the reward, rather than deltas + in X. This way, agents are not discouraged too heavily + from exploring backwards if there is no way to advance + head-on in the level. + """ + def __init__(self, env): + super(AllowBacktracking, self).__init__(env) + self._cur_x = 0 + self._max_x = 0 + + def reset(self, **kwargs): # pylint: disable=E0202 + self._cur_x = 0 + self._max_x = 0 + return self.env.reset(**kwargs) + + def step(self, action): # pylint: disable=E0202 + obs, rew, done, info = self.env.step(action) + self._cur_x += rew + rew = max(0, self._cur_x - self._max_x) + self._max_x = max(self._max_x, self._cur_x) + return obs, rew, done, info diff --git a/baselines/common/runners.py b/baselines/common/runners.py new file mode 100644 index 0000000000000000000000000000000000000000..c30e322d940ca0d403290339805a9b2546c29bf5 --- /dev/null +++ b/baselines/common/runners.py @@ -0,0 +1,19 @@ +import numpy as np +from abc import ABC, abstractmethod + +class AbstractEnvRunner(ABC): + def __init__(self, *, env, model, nsteps): + self.env = env + self.model = model + self.nenv = nenv = env.num_envs if hasattr(env, 'num_envs') else 1 + self.batch_ob_shape = (nenv*nsteps,) + env.observation_space.shape + self.obs = np.zeros((nenv,) + env.observation_space.shape, dtype=env.observation_space.dtype.name) + self.obs[:] = env.reset() + self.nsteps = nsteps + self.states = model.initial_state + self.dones = [False for _ in range(nenv)] + + @abstractmethod + def run(self): + raise NotImplementedError + diff --git a/baselines/common/running_mean_std.py b/baselines/common/running_mean_std.py new file mode 100644 index 0000000000000000000000000000000000000000..c9c5eaf4f5b4d736d5195930b358192819a3b5c6 --- /dev/null +++ b/baselines/common/running_mean_std.py @@ -0,0 +1,187 @@ +import tensorflow as tf +import numpy as np +from baselines.common.tf_util import get_session + +class RunningMeanStd(object): + # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm + def __init__(self, epsilon=1e-4, shape=()): + self.mean = np.zeros(shape, 'float64') + self.var = np.ones(shape, 'float64') + self.count = epsilon + + def update(self, x): + batch_mean = np.mean(x, axis=0) + batch_var = np.var(x, axis=0) + batch_count = x.shape[0] + self.update_from_moments(batch_mean, batch_var, batch_count) + + def update_from_moments(self, batch_mean, batch_var, batch_count): + self.mean, self.var, self.count = update_mean_var_count_from_moments( + self.mean, self.var, self.count, batch_mean, batch_var, batch_count) + +def update_mean_var_count_from_moments(mean, var, count, batch_mean, batch_var, batch_count): + delta = batch_mean - mean + tot_count = count + batch_count + + new_mean = mean + delta * batch_count / tot_count + m_a = var * count + m_b = batch_var * batch_count + M2 = m_a + m_b + np.square(delta) * count * batch_count / tot_count + new_var = M2 / tot_count + new_count = tot_count + + return new_mean, new_var, new_count + + +class TfRunningMeanStd(object): + # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm + ''' + TensorFlow variables-based implmentation of computing running mean and std + Benefit of this implementation is that it can be saved / loaded together with the tensorflow model + ''' + def __init__(self, epsilon=1e-4, shape=(), scope=''): + sess = get_session() + + self._new_mean = tf.compat.v1.placeholder(shape=shape, dtype=tf.float64) + self._new_var = tf.compat.v1.placeholder(shape=shape, dtype=tf.float64) + self._new_count = tf.compat.v1.placeholder(shape=(), dtype=tf.float64) + + + with tf.compat.v1.variable_scope(scope, reuse=tf.compat.v1.AUTO_REUSE): + self._mean = tf.compat.v1.get_variable('mean', initializer=np.zeros(shape, 'float64'), dtype=tf.float64) + self._var = tf.compat.v1.get_variable('std', initializer=np.ones(shape, 'float64'), dtype=tf.float64) + self._count = tf.compat.v1.get_variable('count', initializer=np.full((), epsilon, 'float64'), dtype=tf.float64) + + self.update_ops = tf.group([ + self._var.assign(self._new_var), + self._mean.assign(self._new_mean), + self._count.assign(self._new_count) + ]) + + sess.run(tf.compat.v1.variables_initializer([self._mean, self._var, self._count])) + self.sess = sess + self._set_mean_var_count() + + def _set_mean_var_count(self): + self.mean, self.var, self.count = self.sess.run([self._mean, self._var, self._count]) + + def update(self, x): + batch_mean = np.mean(x, axis=0) + batch_var = np.var(x, axis=0) + batch_count = x.shape[0] + + new_mean, new_var, new_count = update_mean_var_count_from_moments(self.mean, self.var, self.count, batch_mean, batch_var, batch_count) + + self.sess.run(self.update_ops, feed_dict={ + self._new_mean: new_mean, + self._new_var: new_var, + self._new_count: new_count + }) + + self._set_mean_var_count() + + + +def test_runningmeanstd(): + for (x1, x2, x3) in [ + (np.random.randn(3), np.random.randn(4), np.random.randn(5)), + (np.random.randn(3,2), np.random.randn(4,2), np.random.randn(5,2)), + ]: + + rms = RunningMeanStd(epsilon=0.0, shape=x1.shape[1:]) + + x = np.concatenate([x1, x2, x3], axis=0) + ms1 = [x.mean(axis=0), x.var(axis=0)] + rms.update(x1) + rms.update(x2) + rms.update(x3) + ms2 = [rms.mean, rms.var] + + np.testing.assert_allclose(ms1, ms2) + +def test_tf_runningmeanstd(): + for (x1, x2, x3) in [ + (np.random.randn(3), np.random.randn(4), np.random.randn(5)), + (np.random.randn(3,2), np.random.randn(4,2), np.random.randn(5,2)), + ]: + + rms = TfRunningMeanStd(epsilon=0.0, shape=x1.shape[1:], scope='running_mean_std' + str(np.random.randint(0, 128))) + + x = np.concatenate([x1, x2, x3], axis=0) + ms1 = [x.mean(axis=0), x.var(axis=0)] + rms.update(x1) + rms.update(x2) + rms.update(x3) + ms2 = [rms.mean, rms.var] + + np.testing.assert_allclose(ms1, ms2) + + +def profile_tf_runningmeanstd(): + import time + from baselines.common import tf_util + + tf_util.get_session( config=tf.compat.v1.ConfigProto( + inter_op_parallelism_threads=1, + intra_op_parallelism_threads=1, + allow_soft_placement=True + )) + + x = np.random.random((376,)) + + n_trials = 10000 + rms = RunningMeanStd() + tfrms = TfRunningMeanStd() + + tic1 = time.time() + for _ in range(n_trials): + rms.update(x) + + tic2 = time.time() + for _ in range(n_trials): + tfrms.update(x) + + tic3 = time.time() + + print('rms update time ({} trials): {} s'.format(n_trials, tic2 - tic1)) + print('tfrms update time ({} trials): {} s'.format(n_trials, tic3 - tic2)) + + + tic1 = time.time() + for _ in range(n_trials): + z1 = rms.mean + + tic2 = time.time() + for _ in range(n_trials): + z2 = tfrms.mean + + assert z1 == z2 + + tic3 = time.time() + + print('rms get mean time ({} trials): {} s'.format(n_trials, tic2 - tic1)) + print('tfrms get mean time ({} trials): {} s'.format(n_trials, tic3 - tic2)) + + + + ''' + options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) #pylint: disable=E1101 + run_metadata = tf.RunMetadata() + profile_opts = dict(options=options, run_metadata=run_metadata) + + + + from tensorflow.python.client import timeline + fetched_timeline = timeline.Timeline(run_metadata.step_stats) #pylint: disable=E1101 + chrome_trace = fetched_timeline.generate_chrome_trace_format() + outfile = '/tmp/timeline.json' + with open(outfile, 'wt') as f: + f.write(chrome_trace) + print('Successfully saved profile to {}. Exiting.'.format(outfile)) + exit(0) + ''' + + + +if __name__ == '__main__': + profile_tf_runningmeanstd() diff --git a/baselines/common/schedules.py b/baselines/common/schedules.py new file mode 100644 index 0000000000000000000000000000000000000000..9dfff50f950f396945e4602defc8d6e0066f622b --- /dev/null +++ b/baselines/common/schedules.py @@ -0,0 +1,99 @@ +"""This file is used for specifying various schedules that evolve over +time throughout the execution of the algorithm, such as: + - learning rate for the optimizer + - exploration epsilon for the epsilon greedy exploration strategy + - beta parameter for beta parameter in prioritized replay + +Each schedule has a function `value(t)` which returns the current value +of the parameter given the timestep t of the optimization procedure. +""" + + +class Schedule(object): + def value(self, t): + """Value of the schedule at time t""" + raise NotImplementedError() + + +class ConstantSchedule(object): + def __init__(self, value): + """Value remains constant over time. + + Parameters + ---------- + value: float + Constant value of the schedule + """ + self._v = value + + def value(self, t): + """See Schedule.value""" + return self._v + + +def linear_interpolation(l, r, alpha): + return l + alpha * (r - l) + + +class PiecewiseSchedule(object): + def __init__(self, endpoints, interpolation=linear_interpolation, outside_value=None): + """Piecewise schedule. + + endpoints: [(int, int)] + list of pairs `(time, value)` meanining that schedule should output + `value` when `t==time`. All the values for time must be sorted in + an increasing order. When t is between two times, e.g. `(time_a, value_a)` + and `(time_b, value_b)`, such that `time_a <= t < time_b` then value outputs + `interpolation(value_a, value_b, alpha)` where alpha is a fraction of + time passed between `time_a` and `time_b` for time `t`. + interpolation: lambda float, float, float: float + a function that takes value to the left and to the right of t according + to the `endpoints`. Alpha is the fraction of distance from left endpoint to + right endpoint that t has covered. See linear_interpolation for example. + outside_value: float + if the value is requested outside of all the intervals sepecified in + `endpoints` this value is returned. If None then AssertionError is + raised when outside value is requested. + """ + idxes = [e[0] for e in endpoints] + assert idxes == sorted(idxes) + self._interpolation = interpolation + self._outside_value = outside_value + self._endpoints = endpoints + + def value(self, t): + """See Schedule.value""" + for (l_t, l), (r_t, r) in zip(self._endpoints[:-1], self._endpoints[1:]): + if l_t <= t and t < r_t: + alpha = float(t - l_t) / (r_t - l_t) + return self._interpolation(l, r, alpha) + + # t does not belong to any of the pieces, so doom. + assert self._outside_value is not None + return self._outside_value + + +class LinearSchedule(object): + def __init__(self, schedule_timesteps, final_p, initial_p=1.0): + """Linear interpolation between initial_p and final_p over + schedule_timesteps. After this many timesteps pass final_p is + returned. + + Parameters + ---------- + schedule_timesteps: int + Number of timesteps for which to linearly anneal initial_p + to final_p + initial_p: float + initial output value + final_p: float + final output value + """ + self.schedule_timesteps = schedule_timesteps + self.final_p = final_p + self.initial_p = initial_p + + def value(self, t): + """See Schedule.value""" + fraction = min(float(t) / self.schedule_timesteps, 1.0) + return self.initial_p + fraction * (self.final_p - self.initial_p) diff --git a/baselines/common/segment_tree.py b/baselines/common/segment_tree.py new file mode 100644 index 0000000000000000000000000000000000000000..cb386ecdb5bbc1d2c6a4821d29920fc8dcb71adf --- /dev/null +++ b/baselines/common/segment_tree.py @@ -0,0 +1,145 @@ +import operator + + +class SegmentTree(object): + def __init__(self, capacity, operation, neutral_element): + """Build a Segment Tree data structure. + + https://en.wikipedia.org/wiki/Segment_tree + + Can be used as regular array, but with two + important differences: + + a) setting item's value is slightly slower. + It is O(lg capacity) instead of O(1). + b) user has access to an efficient ( O(log segment size) ) + `reduce` operation which reduces `operation` over + a contiguous subsequence of items in the array. + + Paramters + --------- + capacity: int + Total size of the array - must be a power of two. + operation: lambda obj, obj -> obj + and operation for combining elements (eg. sum, max) + must form a mathematical group together with the set of + possible values for array elements (i.e. be associative) + neutral_element: obj + neutral element for the operation above. eg. float('-inf') + for max and 0 for sum. + """ + assert capacity > 0 and capacity & (capacity - 1) == 0, "capacity must be positive and a power of 2." + self._capacity = capacity + self._value = [neutral_element for _ in range(2 * capacity)] + self._operation = operation + + def _reduce_helper(self, start, end, node, node_start, node_end): + if start == node_start and end == node_end: + return self._value[node] + mid = (node_start + node_end) // 2 + if end <= mid: + return self._reduce_helper(start, end, 2 * node, node_start, mid) + else: + if mid + 1 <= start: + return self._reduce_helper(start, end, 2 * node + 1, mid + 1, node_end) + else: + return self._operation( + self._reduce_helper(start, mid, 2 * node, node_start, mid), + self._reduce_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end) + ) + + def reduce(self, start=0, end=None): + """Returns result of applying `self.operation` + to a contiguous subsequence of the array. + + self.operation(arr[start], operation(arr[start+1], operation(... arr[end]))) + + Parameters + ---------- + start: int + beginning of the subsequence + end: int + end of the subsequences + + Returns + ------- + reduced: obj + result of reducing self.operation over the specified range of array elements. + """ + if end is None: + end = self._capacity + if end < 0: + end += self._capacity + end -= 1 + return self._reduce_helper(start, end, 1, 0, self._capacity - 1) + + def __setitem__(self, idx, val): + # index of the leaf + idx += self._capacity + self._value[idx] = val + idx //= 2 + while idx >= 1: + self._value[idx] = self._operation( + self._value[2 * idx], + self._value[2 * idx + 1] + ) + idx //= 2 + + def __getitem__(self, idx): + assert 0 <= idx < self._capacity + return self._value[self._capacity + idx] + + +class SumSegmentTree(SegmentTree): + def __init__(self, capacity): + super(SumSegmentTree, self).__init__( + capacity=capacity, + operation=operator.add, + neutral_element=0.0 + ) + + def sum(self, start=0, end=None): + """Returns arr[start] + ... + arr[end]""" + return super(SumSegmentTree, self).reduce(start, end) + + def find_prefixsum_idx(self, prefixsum): + """Find the highest index `i` in the array such that + sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum + + if array values are probabilities, this function + allows to sample indexes according to the discrete + probability efficiently. + + Parameters + ---------- + perfixsum: float + upperbound on the sum of array prefix + + Returns + ------- + idx: int + highest index satisfying the prefixsum constraint + """ + assert 0 <= prefixsum <= self.sum() + 1e-5 + idx = 1 + while idx < self._capacity: # while non-leaf + if self._value[2 * idx] > prefixsum: + idx = 2 * idx + else: + prefixsum -= self._value[2 * idx] + idx = 2 * idx + 1 + return idx - self._capacity + + +class MinSegmentTree(SegmentTree): + def __init__(self, capacity): + super(MinSegmentTree, self).__init__( + capacity=capacity, + operation=min, + neutral_element=float('inf') + ) + + def min(self, start=0, end=None): + """Returns min(arr[start], ..., arr[end])""" + + return super(MinSegmentTree, self).reduce(start, end) diff --git a/baselines/common/spolicies.py b/baselines/common/spolicies.py new file mode 100644 index 0000000000000000000000000000000000000000..52e0e70f15b0e1188313b5a13430c1c0fead1e77 --- /dev/null +++ b/baselines/common/spolicies.py @@ -0,0 +1,238 @@ +import tensorflow as tf +from baselines.common import tf_util +from baselines.a2c.utils import fc +from baselines.common.distributions import make_pdtype +from baselines.common.input import observation_placeholder, encode_observation +from baselines.common.tf_util import adjust_shape +from baselines.common.mpi_running_mean_std import RunningMeanStd +from baselines.common.models import get_network_builder + +import gym +import numpy as np + +np.set_printoptions(threshold=np.inf) + + +class PolicyWithValue(object): + """ + Encapsulates fields and methods for RL policy and value function estimation with shared parameters + """ + + def __init__(self, env, observations, latent, skill_latent, encoding_indices, vq_latent, pure_vq_latent, + vq_embeddings, pure_latent, + estimate_q=False, vf_latent=None, + sess=None, **tensors): + """ + Parameters: + ---------- + env RL environment + + observations tensorflow placeholder in which the observations will be fed + + latent latent state from which policy distribution parameters should be inferred + + vf_latent latent state from which value function should be inferred (if None, then latent is used) + + skill_latent latent state from which 2D representation should be inferred (if None, then latent is used) + + skill_loss the loss of 2D encoding + + vq_latent latent state from which vq encoding should be inferred (if None, then latent is used) + + vq_loss the loss of the vq encoding + + sess tensorflow session to run calculations in (if None, default session is used) + + **tensors tensorflow tensors for additional attributes such as state or mask + + """ + + self.X = observations + self.pure_latent = pure_latent + self.vq_latent = vq_latent + self.pure_vq_latent = pure_vq_latent + self.vq_embeddings = vq_embeddings + self.ei = encoding_indices + self.state = tf.constant([]) + self.initial_state = None + self.__dict__.update(tensors) + + vf_latent = vf_latent if vf_latent is not None else latent + + vf_latent = tf.compat.v1.layers.flatten(vf_latent) + self.latent = latent = tf.compat.v1.layers.flatten(latent) + self.skill_latent = skill_latent = tf.compat.v1.layers.flatten(skill_latent) + # vq_latent = tf.compat.v1.layers.flatten(vq_latent) + + # Based on the action space, will select what probability distribution type + self.pdtype = make_pdtype(env.action_space) + + self.pd, self.pi = self.pdtype.pdfromlatent(latent, init_scale=0.01) + + # Take an action + self.action = self.pd.sample() + + # Calculate the neg log of our probability + self.neglogp = self.pd.neglogp(self.action) + self.sess = sess or tf.compat.v1.get_default_session() + + if estimate_q: + assert isinstance(env.action_space, gym.spaces.Discrete) + self.q = fc(vf_latent, 'q', env.action_space.n) + self.vf = self.q + else: + self.vf = fc(vf_latent, 'vf', 1) + self.vf = self.vf[:, 0] + + def _evaluate(self, variables, observation, **extra_feed): + sess = self.sess + feed_dict = {self.X: adjust_shape(self.X, observation)} + for inpt_name, data in extra_feed.items(): + if inpt_name in self.__dict__.keys(): + inpt = self.__dict__[inpt_name] + if isinstance(inpt, tf.Tensor) and inpt._op.type == 'Placeholder': + feed_dict[inpt] = adjust_shape(inpt, data) + + return sess.run(variables, feed_dict) + + def step(self, observation, **extra_feed): + """ + Compute next action(s) given the observation(s) + + Parameters: + ---------- + + observation observation data (either single or a batch) + + train_skill if needing to train skills + + **extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__) + + Returns: + ------- + (action, value estimate, next state, negative log likelihood of the action under current policy parameters) tuple + """ + + a, v, pure_latent, vq_latent, pure_vq_latent, vq_embeddings, encoding_indices, state, neglogp, sl, lat = self._evaluate( + [self.action, self.vf, self.pure_latent, self.vq_latent, self.pure_vq_latent, self.vq_embeddings, self.ei, + self.state, self.neglogp, self.skill_latent, self.latent], observation, + **extra_feed) + + if state.size == 0: + state = None + return a, v, encoding_indices, state, neglogp + + def skill_step(self, observation, **extra_feed): + """ + Compute next action(s) given the observation(s) + + Parameters: + ---------- + + observation observation data (either single or a batch) + + train_skill if needing to train skills + + **extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__) + + Returns: + ------- + (action, value estimate, next state, negative log likelihood of the action under current policy parameters) tuple + """ + + a, v, pure_latent, vq_latent, pure_vq_latent, vq_embeddings, encoding_indices, state, neglogp, sl, lat = self._evaluate( + [self.action, self.vf, self.pure_latent, self.vq_latent, self.pure_vq_latent, self.vq_embeddings, self.ei, + self.state, self.neglogp, self.skill_latent, self.latent], observation, + **extra_feed) + + return a, v, pure_latent, vq_latent, pure_vq_latent, vq_embeddings, encoding_indices, sl, lat + + def value(self, ob, *args, **kwargs): + """ + Compute value estimate(s) given the observation(s) + + Parameters: + ---------- + + observation observation data (either single or a batch) + + **extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__) + + Returns: + ------- + value estimate + """ + return self._evaluate(self.vf, ob, *args, **kwargs) + + def save(self, save_path): + tf_util.save_state(save_path, sess=self.sess) + + def load(self, load_path): + tf_util.load_state(load_path, sess=self.sess) + + +def build_policy(env, policy_network, value_network=None, normalize_observations=False, estimate_q=False, + **policy_kwargs): + if isinstance(policy_network, str): + network_type = policy_network + policy_network = get_network_builder(network_type)(**policy_kwargs) + + def policy_fn(nbatch=None, nsteps=None, sess=None, observ_placeholder=None): + ob_space = env.observation_space + + X = observ_placeholder if observ_placeholder is not None else observation_placeholder(ob_space, + batch_size=nbatch) + + extra_tensors = {} + + if normalize_observations and X.dtype == tf.float32: + encoded_x, rms = _normalize_clip_observation(X) + extra_tensors['rms'] = rms + else: + encoded_x = X + + encoded_x = encode_observation(ob_space, encoded_x) + + with tf.compat.v1.variable_scope('pi', reuse=tf.compat.v1.AUTO_REUSE): + policy_latent = policy_network(encoded_x) + if isinstance(policy_latent, tuple): + policy_latent, skill_latent, pure_latent, vq_latent, pure_vq_latent, vq_embeddings, encoding_indices = policy_latent + + _v_net = value_network + + if _v_net is None or _v_net == 'shared': + vf_latent = policy_latent + else: + if _v_net == 'copy': + _v_net = policy_network + else: + assert callable(_v_net) + + with tf.compat.v1.variable_scope('vf', reuse=tf.compat.v1.AUTO_REUSE): + # TODO recurrent architectures are not supported with value_network=copy yet + vf_latent = _v_net(encoded_x) + + policy = PolicyWithValue( + env=env, + observations=X, + latent=policy_latent, + vf_latent=vf_latent, + skill_latent=skill_latent, + pure_latent=pure_latent, + vq_latent=vq_latent, + pure_vq_latent=pure_vq_latent, + vq_embeddings=vq_embeddings, + encoding_indices=encoding_indices, + sess=sess, + estimate_q=estimate_q, + **extra_tensors + ) + return policy + + return policy_fn + + +def _normalize_clip_observation(x, clip_range=[-5.0, 5.0]): + rms = RunningMeanStd(shape=x.shape[1:]) + norm_x = tf.clip_by_value((x - rms.mean) / rms.std, min(clip_range), max(clip_range)) + return norm_x, rms diff --git a/baselines/common/test_mpi_util.py b/baselines/common/test_mpi_util.py new file mode 100644 index 0000000000000000000000000000000000000000..8b944201449b3d38d38e8f71d7f159f7756ad841 --- /dev/null +++ b/baselines/common/test_mpi_util.py @@ -0,0 +1,29 @@ +from baselines.common import mpi_util +from baselines import logger +from baselines.common.tests.test_with_mpi import with_mpi +try: + from mpi4py import MPI +except ImportError: + MPI = None + +@with_mpi() +def test_mpi_weighted_mean(): + comm = MPI.COMM_WORLD + with logger.scoped_configure(comm=comm): + if comm.rank == 0: + name2valcount = {'a' : (10, 2), 'b' : (20,3)} + elif comm.rank == 1: + name2valcount = {'a' : (19, 1), 'c' : (42,3)} + else: + raise NotImplementedError + d = mpi_util.mpi_weighted_mean(comm, name2valcount) + correctval = {'a' : (10 * 2 + 19) / 3.0, 'b' : 20, 'c' : 42} + if comm.rank == 0: + assert d == correctval, '{} != {}'.format(d, correctval) + + for name, (val, count) in name2valcount.items(): + for _ in range(count): + logger.logkv_mean(name, val) + d2 = logger.dumpkvs() + if comm.rank == 0: + assert d2 == correctval diff --git a/baselines/common/tests/__init__.py b/baselines/common/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a6561a2684d3847c19f3b77e630a1e1168dccf80 --- /dev/null +++ b/baselines/common/tests/__init__.py @@ -0,0 +1,2 @@ +import os, pytest +mark_slow = pytest.mark.skipif(not os.getenv('RUNSLOW'), reason='slow') \ No newline at end of file diff --git a/baselines/common/tests/envs/__init__.py b/baselines/common/tests/envs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/common/tests/envs/fixed_sequence_env.py b/baselines/common/tests/envs/fixed_sequence_env.py new file mode 100644 index 0000000000000000000000000000000000000000..b3fe39656ba18c8294c28dd76b039f81e46e2553 --- /dev/null +++ b/baselines/common/tests/envs/fixed_sequence_env.py @@ -0,0 +1,43 @@ +import numpy as np +from gym import Env +from gym.spaces import Discrete + + +class FixedSequenceEnv(Env): + def __init__( + self, + n_actions=10, + episode_len=100 + ): + self.action_space = Discrete(n_actions) + self.observation_space = Discrete(1) + self.np_random = np.random.RandomState(0) + self.episode_len = episode_len + self.sequence = [self.np_random.randint(0, self.action_space.n) + for _ in range(self.episode_len)] + self.time = 0 + + + def reset(self): + self.time = 0 + return 0 + + def step(self, actions): + rew = self._get_reward(actions) + self._choose_next_state() + done = False + if self.episode_len and self.time >= self.episode_len: + done = True + + return 0, rew, done, {} + + def seed(self, seed=None): + self.np_random.seed(seed) + + def _choose_next_state(self): + self.time += 1 + + def _get_reward(self, actions): + return 1 if actions == self.sequence[self.time] else 0 + + diff --git a/baselines/common/tests/envs/identity_env.py b/baselines/common/tests/envs/identity_env.py new file mode 100644 index 0000000000000000000000000000000000000000..fb2dca691a95ce55f47e1759d164eba9a50ef3f2 --- /dev/null +++ b/baselines/common/tests/envs/identity_env.py @@ -0,0 +1,90 @@ +import numpy as np +from abc import abstractmethod +from gym import Env +from gym.spaces import MultiDiscrete, Discrete, Box +from collections import deque + +class IdentityEnv(Env): + def __init__( + self, + episode_len=None, + delay=0, + zero_first_rewards=True + ): + + self.observation_space = self.action_space + self.episode_len = episode_len + self.time = 0 + self.delay = delay + self.zero_first_rewards = zero_first_rewards + self.q = deque(maxlen=delay+1) + + def reset(self): + self.q.clear() + for _ in range(self.delay + 1): + self.q.append(self.action_space.sample()) + self.time = 0 + + return self.q[-1] + + def step(self, actions): + rew = self._get_reward(self.q.popleft(), actions) + if self.zero_first_rewards and self.time < self.delay: + rew = 0 + self.q.append(self.action_space.sample()) + self.time += 1 + done = self.episode_len is not None and self.time >= self.episode_len + return self.q[-1], rew, done, {} + + def seed(self, seed=None): + self.action_space.seed(seed) + + @abstractmethod + def _get_reward(self, state, actions): + raise NotImplementedError + + +class DiscreteIdentityEnv(IdentityEnv): + def __init__( + self, + dim, + episode_len=None, + delay=0, + zero_first_rewards=True + ): + + self.action_space = Discrete(dim) + super().__init__(episode_len=episode_len, delay=delay, zero_first_rewards=zero_first_rewards) + + def _get_reward(self, state, actions): + return 1 if state == actions else 0 + +class MultiDiscreteIdentityEnv(IdentityEnv): + def __init__( + self, + dims, + episode_len=None, + delay=0, + ): + + self.action_space = MultiDiscrete(dims) + super().__init__(episode_len=episode_len, delay=delay) + + def _get_reward(self, state, actions): + return 1 if all(state == actions) else 0 + + +class BoxIdentityEnv(IdentityEnv): + def __init__( + self, + shape, + episode_len=None, + ): + + self.action_space = Box(low=-1.0, high=1.0, shape=shape, dtype=np.float32) + super().__init__(episode_len=episode_len) + + def _get_reward(self, state, actions): + diff = actions - state + diff = diff[:] + return -0.5 * np.dot(diff, diff) diff --git a/baselines/common/tests/envs/identity_env_test.py b/baselines/common/tests/envs/identity_env_test.py new file mode 100644 index 0000000000000000000000000000000000000000..c73ee5713109677176fe28e3eadb49bdae746254 --- /dev/null +++ b/baselines/common/tests/envs/identity_env_test.py @@ -0,0 +1,36 @@ +from baselines.common.tests.envs.identity_env import DiscreteIdentityEnv + + +def test_discrete_nodelay(): + nsteps = 100 + eplen = 50 + env = DiscreteIdentityEnv(10, episode_len=eplen) + ob = env.reset() + for t in range(nsteps): + action = env.action_space.sample() + next_ob, rew, done, info = env.step(action) + assert rew == (1 if action == ob else 0) + if (t + 1) % eplen == 0: + assert done + next_ob = env.reset() + else: + assert not done + ob = next_ob + +def test_discrete_delay1(): + eplen = 50 + env = DiscreteIdentityEnv(10, episode_len=eplen, delay=1) + ob = env.reset() + prev_ob = None + for t in range(eplen): + action = env.action_space.sample() + next_ob, rew, done, info = env.step(action) + if t > 0: + assert rew == (1 if action == prev_ob else 0) + else: + assert rew == 0 + prev_ob = ob + ob = next_ob + if t < eplen - 1: + assert not done + assert done diff --git a/baselines/common/tests/envs/mnist_env.py b/baselines/common/tests/envs/mnist_env.py new file mode 100644 index 0000000000000000000000000000000000000000..cc0bde0619f5013f68abb37b92a841c791012727 --- /dev/null +++ b/baselines/common/tests/envs/mnist_env.py @@ -0,0 +1,71 @@ +import os.path as osp +import numpy as np +import tempfile +from gym import Env +from gym.spaces import Discrete, Box + + + +class MnistEnv(Env): + def __init__( + self, + episode_len=None, + no_images=None + ): + import filelock + from tensorflow.examples.tutorials.mnist import input_data + # we could use temporary directory for this with a context manager and + # TemporaryDirecotry, but then each test that uses mnist would re-download the data + # this way the data is not cleaned up, but we only download it once per machine + mnist_path = osp.join(tempfile.gettempdir(), 'MNIST_data') + with filelock.FileLock(mnist_path + '.lock'): + self.mnist = input_data.read_data_sets(mnist_path) + + self.np_random = np.random.RandomState() + + self.observation_space = Box(low=0.0, high=1.0, shape=(28,28,1)) + self.action_space = Discrete(10) + self.episode_len = episode_len + self.time = 0 + self.no_images = no_images + + self.train_mode() + self.reset() + + def reset(self): + self._choose_next_state() + self.time = 0 + + return self.state[0] + + def step(self, actions): + rew = self._get_reward(actions) + self._choose_next_state() + done = False + if self.episode_len and self.time >= self.episode_len: + rew = 0 + done = True + + return self.state[0], rew, done, {} + + def seed(self, seed=None): + self.np_random.seed(seed) + + def train_mode(self): + self.dataset = self.mnist.train + + def test_mode(self): + self.dataset = self.mnist.test + + def _choose_next_state(self): + max_index = (self.no_images if self.no_images is not None else self.dataset.num_examples) - 1 + index = self.np_random.randint(0, max_index) + image = self.dataset.images[index].reshape(28,28,1)*255 + label = self.dataset.labels[index] + self.state = (image, label) + self.time += 1 + + def _get_reward(self, actions): + return 1 if self.state[1] == actions else 0 + + diff --git a/baselines/common/tests/test_cartpole.py b/baselines/common/tests/test_cartpole.py new file mode 100644 index 0000000000000000000000000000000000000000..f9d5ac6630a0fd28db1468d4a94822ac20a32202 --- /dev/null +++ b/baselines/common/tests/test_cartpole.py @@ -0,0 +1,45 @@ +import pytest +import gym + +from baselines.run import get_learn_function +from baselines.common.tests.util import reward_per_episode_test +from baselines.common.tests import mark_slow + +common_kwargs = dict( + total_timesteps=30000, + network='mlp', + gamma=1.0, + seed=0, +) + +learn_kwargs = { + 'a2c' : dict(nsteps=32, value_network='copy', lr=0.05), + 'acer': dict(value_network='copy'), + 'acktr': dict(nsteps=32, value_network='copy', is_async=False), + 'deepq': dict(total_timesteps=20000), + 'ppo2': dict(value_network='copy'), + 'trpo_mpi': {} +} + +@mark_slow +@pytest.mark.parametrize("alg", learn_kwargs.keys()) +def test_cartpole(alg): + ''' + Test if the algorithm (with an mlp policy) + can learn to balance the cartpole + ''' + + kwargs = common_kwargs.copy() + kwargs.update(learn_kwargs[alg]) + + learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs) + def env_fn(): + + env = gym.make('CartPole-v0') + env.seed(0) + return env + + reward_per_episode_test(env_fn, learn_fn, 100) + +if __name__ == '__main__': + test_cartpole('acer') diff --git a/baselines/common/tests/test_doc_examples.py b/baselines/common/tests/test_doc_examples.py new file mode 100644 index 0000000000000000000000000000000000000000..ac2d75ee5c1d61368da4a1fe9c29adb72d4de17a --- /dev/null +++ b/baselines/common/tests/test_doc_examples.py @@ -0,0 +1,48 @@ +import pytest +try: + import mujoco_py + _mujoco_present = True +except BaseException: + mujoco_py = None + _mujoco_present = False + + +@pytest.mark.skipif( + not _mujoco_present, + reason='error loading mujoco - either mujoco / mujoco key not present, or LD_LIBRARY_PATH is not pointing to mujoco library' +) +def test_lstm_example(): + import tensorflow as tf + from baselines.common import policies, models, cmd_util + from baselines.common.vec_env.dummy_vec_env import DummyVecEnv + + # create vectorized environment + venv = DummyVecEnv([lambda: cmd_util.make_mujoco_env('Reacher-v2', seed=0)]) + + with tf.compat.v1.Session() as sess: + # build policy based on lstm network with 128 units + policy = policies.build_policy(venv, models.lstm(128))(nbatch=1, nsteps=1) + + # initialize tensorflow variables + sess.run(tf.compat.v1.global_variables_initializer()) + + # prepare environment variables + ob = venv.reset() + state = policy.initial_state + done = [False] + step_counter = 0 + + # run a single episode until the end (i.e. until done) + while True: + action, _, state, _ = policy.step(ob, S=state, M=done) + ob, reward, done, _ = venv.step(action) + step_counter += 1 + if done: + break + + + assert step_counter > 5 + + + + diff --git a/baselines/common/tests/test_env_after_learn.py b/baselines/common/tests/test_env_after_learn.py new file mode 100644 index 0000000000000000000000000000000000000000..eea1e00cd071f7dee09fe378d06b87580c43fc32 --- /dev/null +++ b/baselines/common/tests/test_env_after_learn.py @@ -0,0 +1,27 @@ +import pytest +import gym +import tensorflow as tf + +from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv +from baselines.run import get_learn_function +from baselines.common.tf_util import make_session + +algos = ['a2c', 'acer', 'acktr', 'deepq', 'ppo2', 'trpo_mpi'] + +@pytest.mark.parametrize('algo', algos) +def test_env_after_learn(algo): + def make_env(): + # acktr requires too much RAM, fails on travis + env = gym.make('CartPole-v1' if algo == 'acktr' else 'PongNoFrameskip-v4') + return env + + make_session(make_default=True, graph=tf.Graph()) + env = SubprocVecEnv([make_env]) + + learn = get_learn_function(algo) + + # Commenting out the following line resolves the issue, though crash happens at env.reset(). + learn(network='mlp', env=env, total_timesteps=0, load_path=None, seed=None) + + env.reset() + env.close() diff --git a/baselines/common/tests/test_fetchreach.py b/baselines/common/tests/test_fetchreach.py new file mode 100644 index 0000000000000000000000000000000000000000..8bcd32b5f50ba424c605dee7bd373591c52c7d3d --- /dev/null +++ b/baselines/common/tests/test_fetchreach.py @@ -0,0 +1,40 @@ +import pytest +import gym + +from baselines.run import get_learn_function +from baselines.common.tests.util import reward_per_episode_test +from baselines.common.tests import mark_slow + +pytest.importorskip('mujoco_py') + +common_kwargs = dict( + network='mlp', + seed=0, +) + +learn_kwargs = { + 'her': dict(total_timesteps=2000) +} + +@mark_slow +@pytest.mark.parametrize("alg", learn_kwargs.keys()) +def test_fetchreach(alg): + ''' + Test if the algorithm (with an mlp policy) + can learn the FetchReach task + ''' + + kwargs = common_kwargs.copy() + kwargs.update(learn_kwargs[alg]) + + learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs) + def env_fn(): + + env = gym.make('FetchReach-v1') + env.seed(0) + return env + + reward_per_episode_test(env_fn, learn_fn, -15) + +if __name__ == '__main__': + test_fetchreach('her') diff --git a/baselines/common/tests/test_fixed_sequence.py b/baselines/common/tests/test_fixed_sequence.py new file mode 100644 index 0000000000000000000000000000000000000000..68ee8d33b3548e44420b320e39aabafff3255684 --- /dev/null +++ b/baselines/common/tests/test_fixed_sequence.py @@ -0,0 +1,52 @@ +import pytest +from baselines.common.tests.envs.fixed_sequence_env import FixedSequenceEnv + +from baselines.common.tests.util import simple_test +from baselines.run import get_learn_function +from baselines.common.tests import mark_slow + + +common_kwargs = dict( + seed=0, + total_timesteps=50000, +) + +learn_kwargs = { + 'a2c': {}, + 'ppo2': dict(nsteps=10, ent_coef=0.0, nminibatches=1), + # TODO enable sequential models for trpo_mpi (proper handling of nbatch and nsteps) + # github issue: https://github.com/openai/baselines/issues/188 + # 'trpo_mpi': lambda e, p: trpo_mpi.learn(policy_fn=p(env=e), env=e, max_timesteps=30000, timesteps_per_batch=100, cg_iters=10, gamma=0.9, lam=1.0, max_kl=0.001) +} + + +alg_list = learn_kwargs.keys() +rnn_list = ['lstm'] + +@mark_slow +@pytest.mark.parametrize("alg", alg_list) +@pytest.mark.parametrize("rnn", rnn_list) +def test_fixed_sequence(alg, rnn): + ''' + Test if the algorithm (with a given policy) + can learn an identity transformation (i.e. return observation as an action) + ''' + + kwargs = learn_kwargs[alg] + kwargs.update(common_kwargs) + + env_fn = lambda: FixedSequenceEnv(n_actions=10, episode_len=5) + learn = lambda e: get_learn_function(alg)( + env=e, + network=rnn, + **kwargs + ) + + simple_test(env_fn, learn, 0.7) + + +if __name__ == '__main__': + test_fixed_sequence('ppo2', 'lstm') + + + diff --git a/baselines/common/tests/test_identity.py b/baselines/common/tests/test_identity.py new file mode 100644 index 0000000000000000000000000000000000000000..6b66a661297735a72c1b8f2bb98da2c9ba8c1b15 --- /dev/null +++ b/baselines/common/tests/test_identity.py @@ -0,0 +1,76 @@ +import pytest +from baselines.common.tests.envs.identity_env import DiscreteIdentityEnv, BoxIdentityEnv, MultiDiscreteIdentityEnv +from baselines.run import get_learn_function +from baselines.common.tests.util import simple_test +from baselines.common.tests import mark_slow + +common_kwargs = dict( + total_timesteps=30000, + network='mlp', + gamma=0.9, + seed=0, +) + +learn_kwargs = { + 'a2c' : {}, + 'acktr': {}, + 'deepq': {}, + 'ddpg': dict(layer_norm=True), + 'ppo2': dict(lr=1e-3, nsteps=64, ent_coef=0.0), + 'trpo_mpi': dict(timesteps_per_batch=100, cg_iters=10, gamma=0.9, lam=1.0, max_kl=0.01) +} + + +algos_disc = ['a2c', 'acktr', 'deepq', 'ppo2', 'trpo_mpi'] +algos_multidisc = ['a2c', 'acktr', 'ppo2', 'trpo_mpi'] +algos_cont = ['a2c', 'acktr', 'ddpg', 'ppo2', 'trpo_mpi'] + +@mark_slow +@pytest.mark.parametrize("alg", algos_disc) +def test_discrete_identity(alg): + ''' + Test if the algorithm (with an mlp policy) + can learn an identity transformation (i.e. return observation as an action) + ''' + + kwargs = learn_kwargs[alg] + kwargs.update(common_kwargs) + + learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs) + env_fn = lambda: DiscreteIdentityEnv(10, episode_len=100) + simple_test(env_fn, learn_fn, 0.9) + +@mark_slow +@pytest.mark.parametrize("alg", algos_multidisc) +def test_multidiscrete_identity(alg): + ''' + Test if the algorithm (with an mlp policy) + can learn an identity transformation (i.e. return observation as an action) + ''' + + kwargs = learn_kwargs[alg] + kwargs.update(common_kwargs) + + learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs) + env_fn = lambda: MultiDiscreteIdentityEnv((3,3), episode_len=100) + simple_test(env_fn, learn_fn, 0.9) + +@mark_slow +@pytest.mark.parametrize("alg", algos_cont) +def test_continuous_identity(alg): + ''' + Test if the algorithm (with an mlp policy) + can learn an identity transformation (i.e. return observation as an action) + to a required precision + ''' + + kwargs = learn_kwargs[alg] + kwargs.update(common_kwargs) + learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs) + + env_fn = lambda: BoxIdentityEnv((1,), episode_len=100) + simple_test(env_fn, learn_fn, -0.1) + +if __name__ == '__main__': + test_multidiscrete_identity('acktr') + diff --git a/baselines/common/tests/test_mnist.py b/baselines/common/tests/test_mnist.py new file mode 100644 index 0000000000000000000000000000000000000000..06a4e2b34cc6fc708a401ccf19ca4e3f0641731d --- /dev/null +++ b/baselines/common/tests/test_mnist.py @@ -0,0 +1,49 @@ +import pytest + +# from baselines.acer import acer_simple as acer +from baselines.common.tests.envs.mnist_env import MnistEnv +from baselines.common.tests.util import simple_test +from baselines.run import get_learn_function +from baselines.common.tests import mark_slow + +# TODO investigate a2c and ppo2 failures - is it due to bad hyperparameters for this problem? +# GitHub issue https://github.com/openai/baselines/issues/189 +common_kwargs = { + 'seed': 0, + 'network':'cnn', + 'gamma':0.9, + 'pad':'SAME' +} + +learn_args = { + 'a2c': dict(total_timesteps=50000), + 'acer': dict(total_timesteps=20000), + 'deepq': dict(total_timesteps=5000), + 'acktr': dict(total_timesteps=30000), + 'ppo2': dict(total_timesteps=50000, lr=1e-3, nsteps=128, ent_coef=0.0), + 'trpo_mpi': dict(total_timesteps=80000, timesteps_per_batch=100, cg_iters=10, lam=1.0, max_kl=0.001) +} + + +#tests pass, but are too slow on travis. Same algorithms are covered +# by other tests with less compute-hungry nn's and by benchmarks +@pytest.mark.skip +@mark_slow +@pytest.mark.parametrize("alg", learn_args.keys()) +def test_mnist(alg): + ''' + Test if the algorithm can learn to classify MNIST digits. + Uses CNN policy. + ''' + + learn_kwargs = learn_args[alg] + learn_kwargs.update(common_kwargs) + + learn = get_learn_function(alg) + learn_fn = lambda e: learn(env=e, **learn_kwargs) + env_fn = lambda: MnistEnv(episode_len=100) + + simple_test(env_fn, learn_fn, 0.6) + +if __name__ == '__main__': + test_mnist('acer') diff --git a/baselines/common/tests/test_plot_util.py b/baselines/common/tests/test_plot_util.py new file mode 100644 index 0000000000000000000000000000000000000000..be3330838dcc6c1ff5291a82eda44fbc65c98423 --- /dev/null +++ b/baselines/common/tests/test_plot_util.py @@ -0,0 +1,17 @@ +# smoke tests of plot_util +from baselines.common import plot_util as pu +from baselines.common.tests.util import smoketest + + +def test_plot_util(): + nruns = 4 + logdirs = [smoketest('--alg=ppo2 --env=CartPole-v0 --num_timesteps=10000') for _ in range(nruns)] + data = pu.load_results(logdirs) + assert len(data) == 4 + + _, axes = pu.plot_results(data[:1]); assert len(axes) == 1 + _, axes = pu.plot_results(data, tiling='vertical'); assert axes.shape==(4,1) + _, axes = pu.plot_results(data, tiling='horizontal'); assert axes.shape==(1,4) + _, axes = pu.plot_results(data, tiling='symmetric'); assert axes.shape==(2,2) + _, axes = pu.plot_results(data, split_fn=lambda _: ''); assert len(axes) == 1 + diff --git a/baselines/common/tests/test_schedules.py b/baselines/common/tests/test_schedules.py new file mode 100644 index 0000000000000000000000000000000000000000..4e8d02d291c5ff03d1c7ca21f8fa42c1175cbc1b --- /dev/null +++ b/baselines/common/tests/test_schedules.py @@ -0,0 +1,26 @@ +import numpy as np + +from baselines.common.schedules import ConstantSchedule, PiecewiseSchedule + + +def test_piecewise_schedule(): + ps = PiecewiseSchedule([(-5, 100), (5, 200), (10, 50), (100, 50), (200, -50)], outside_value=500) + + assert np.isclose(ps.value(-10), 500) + assert np.isclose(ps.value(0), 150) + assert np.isclose(ps.value(5), 200) + assert np.isclose(ps.value(9), 80) + assert np.isclose(ps.value(50), 50) + assert np.isclose(ps.value(80), 50) + assert np.isclose(ps.value(150), 0) + assert np.isclose(ps.value(175), -25) + assert np.isclose(ps.value(201), 500) + assert np.isclose(ps.value(500), 500) + + assert np.isclose(ps.value(200 - 1e-10), -50) + + +def test_constant_schedule(): + cs = ConstantSchedule(5) + for i in range(-100, 100): + assert np.isclose(cs.value(i), 5) diff --git a/baselines/common/tests/test_segment_tree.py b/baselines/common/tests/test_segment_tree.py new file mode 100644 index 0000000000000000000000000000000000000000..700e0bb456a315966921b65bb66852e1b4bd11ed --- /dev/null +++ b/baselines/common/tests/test_segment_tree.py @@ -0,0 +1,103 @@ +import numpy as np + +from baselines.common.segment_tree import SumSegmentTree, MinSegmentTree + + +def test_tree_set(): + tree = SumSegmentTree(4) + + tree[2] = 1.0 + tree[3] = 3.0 + + assert np.isclose(tree.sum(), 4.0) + assert np.isclose(tree.sum(0, 2), 0.0) + assert np.isclose(tree.sum(0, 3), 1.0) + assert np.isclose(tree.sum(2, 3), 1.0) + assert np.isclose(tree.sum(2, -1), 1.0) + assert np.isclose(tree.sum(2, 4), 4.0) + + +def test_tree_set_overlap(): + tree = SumSegmentTree(4) + + tree[2] = 1.0 + tree[2] = 3.0 + + assert np.isclose(tree.sum(), 3.0) + assert np.isclose(tree.sum(2, 3), 3.0) + assert np.isclose(tree.sum(2, -1), 3.0) + assert np.isclose(tree.sum(2, 4), 3.0) + assert np.isclose(tree.sum(1, 2), 0.0) + + +def test_prefixsum_idx(): + tree = SumSegmentTree(4) + + tree[2] = 1.0 + tree[3] = 3.0 + + assert tree.find_prefixsum_idx(0.0) == 2 + assert tree.find_prefixsum_idx(0.5) == 2 + assert tree.find_prefixsum_idx(0.99) == 2 + assert tree.find_prefixsum_idx(1.01) == 3 + assert tree.find_prefixsum_idx(3.00) == 3 + assert tree.find_prefixsum_idx(4.00) == 3 + + +def test_prefixsum_idx2(): + tree = SumSegmentTree(4) + + tree[0] = 0.5 + tree[1] = 1.0 + tree[2] = 1.0 + tree[3] = 3.0 + + assert tree.find_prefixsum_idx(0.00) == 0 + assert tree.find_prefixsum_idx(0.55) == 1 + assert tree.find_prefixsum_idx(0.99) == 1 + assert tree.find_prefixsum_idx(1.51) == 2 + assert tree.find_prefixsum_idx(3.00) == 3 + assert tree.find_prefixsum_idx(5.50) == 3 + + +def test_max_interval_tree(): + tree = MinSegmentTree(4) + + tree[0] = 1.0 + tree[2] = 0.5 + tree[3] = 3.0 + + assert np.isclose(tree.min(), 0.5) + assert np.isclose(tree.min(0, 2), 1.0) + assert np.isclose(tree.min(0, 3), 0.5) + assert np.isclose(tree.min(0, -1), 0.5) + assert np.isclose(tree.min(2, 4), 0.5) + assert np.isclose(tree.min(3, 4), 3.0) + + tree[2] = 0.7 + + assert np.isclose(tree.min(), 0.7) + assert np.isclose(tree.min(0, 2), 1.0) + assert np.isclose(tree.min(0, 3), 0.7) + assert np.isclose(tree.min(0, -1), 0.7) + assert np.isclose(tree.min(2, 4), 0.7) + assert np.isclose(tree.min(3, 4), 3.0) + + tree[2] = 4.0 + + assert np.isclose(tree.min(), 1.0) + assert np.isclose(tree.min(0, 2), 1.0) + assert np.isclose(tree.min(0, 3), 1.0) + assert np.isclose(tree.min(0, -1), 1.0) + assert np.isclose(tree.min(2, 4), 3.0) + assert np.isclose(tree.min(2, 3), 4.0) + assert np.isclose(tree.min(2, -1), 4.0) + assert np.isclose(tree.min(3, 4), 3.0) + + +if __name__ == '__main__': + test_tree_set() + test_tree_set_overlap() + test_prefixsum_idx() + test_prefixsum_idx2() + test_max_interval_tree() diff --git a/baselines/common/tests/test_serialization.py b/baselines/common/tests/test_serialization.py new file mode 100644 index 0000000000000000000000000000000000000000..1a2c1529944c43b320b06a8b254ee35b0da4e1fe --- /dev/null +++ b/baselines/common/tests/test_serialization.py @@ -0,0 +1,139 @@ +import os +import gym +import tempfile +import pytest +import tensorflow as tf +import numpy as np + +from baselines.common.tests.envs.mnist_env import MnistEnv +from baselines.common.vec_env.dummy_vec_env import DummyVecEnv +from baselines.run import get_learn_function +from baselines.common.tf_util import make_session, get_session + +from functools import partial + + +learn_kwargs = { + 'deepq': {}, + 'a2c': {}, + 'acktr': {}, + 'acer': {}, + 'ppo2': {'nminibatches': 1, 'nsteps': 10}, + 'trpo_mpi': {}, +} + +network_kwargs = { + 'mlp': {}, + 'cnn': {'pad': 'SAME'}, + 'lstm': {}, + 'cnn_lnlstm': {'pad': 'SAME'} +} + + +@pytest.mark.parametrize("learn_fn", learn_kwargs.keys()) +@pytest.mark.parametrize("network_fn", network_kwargs.keys()) +def test_serialization(learn_fn, network_fn): + ''' + Test if the trained model can be serialized + ''' + + + if network_fn.endswith('lstm') and learn_fn in ['acer', 'acktr', 'trpo_mpi', 'deepq']: + # TODO make acktr work with recurrent policies + # and test + # github issue: https://github.com/openai/baselines/issues/660 + return + + def make_env(): + env = MnistEnv(episode_len=100) + env.seed(10) + return env + + env = DummyVecEnv([make_env]) + ob = env.reset().copy() + learn = get_learn_function(learn_fn) + + kwargs = {} + kwargs.update(network_kwargs[network_fn]) + kwargs.update(learn_kwargs[learn_fn]) + + + learn = partial(learn, env=env, network=network_fn, seed=0, **kwargs) + + with tempfile.TemporaryDirectory() as td: + model_path = os.path.join(td, 'serialization_test_model') + + with tf.Graph().as_default(), make_session().as_default(): + model = learn(total_timesteps=100) + model.save(model_path) + mean1, std1 = _get_action_stats(model, ob) + variables_dict1 = _serialize_variables() + + with tf.Graph().as_default(), make_session().as_default(): + model = learn(total_timesteps=0, load_path=model_path) + mean2, std2 = _get_action_stats(model, ob) + variables_dict2 = _serialize_variables() + + for k, v in variables_dict1.items(): + np.testing.assert_allclose(v, variables_dict2[k], atol=0.01, + err_msg='saved and loaded variable {} value mismatch'.format(k)) + + np.testing.assert_allclose(mean1, mean2, atol=0.5) + np.testing.assert_allclose(std1, std2, atol=0.5) + + +@pytest.mark.parametrize("learn_fn", learn_kwargs.keys()) +@pytest.mark.parametrize("network_fn", ['mlp']) +def test_coexistence(learn_fn, network_fn): + ''' + Test if more than one model can exist at a time + ''' + + if learn_fn == 'deepq': + # TODO enable multiple DQN models to be useable at the same time + # github issue https://github.com/openai/baselines/issues/656 + return + + if network_fn.endswith('lstm') and learn_fn in ['acktr', 'trpo_mpi', 'deepq']: + # TODO make acktr work with recurrent policies + # and test + # github issue: https://github.com/openai/baselines/issues/660 + return + + env = DummyVecEnv([lambda: gym.make('CartPole-v0')]) + learn = get_learn_function(learn_fn) + + kwargs = {} + kwargs.update(network_kwargs[network_fn]) + kwargs.update(learn_kwargs[learn_fn]) + + learn = partial(learn, env=env, network=network_fn, total_timesteps=0, **kwargs) + make_session(make_default=True, graph=tf.Graph()) + model1 = learn(seed=1) + make_session(make_default=True, graph=tf.Graph()) + model2 = learn(seed=2) + + model1.step(env.observation_space.sample()) + model2.step(env.observation_space.sample()) + + + +def _serialize_variables(): + sess = get_session() + variables = tf.compat.v1.trainable_variables() + values = sess.run(variables) + return {var.name: value for var, value in zip(variables, values)} + + +def _get_action_stats(model, ob): + ntrials = 1000 + if model.initial_state is None or model.initial_state == []: + actions = np.array([model.step(ob)[0] for _ in range(ntrials)]) + else: + actions = np.array([model.step(ob, S=model.initial_state, M=[False])[0] for _ in range(ntrials)]) + + mean = np.mean(actions, axis=0) + std = np.std(actions, axis=0) + + return mean, std + diff --git a/baselines/common/tests/test_tf_util.py b/baselines/common/tests/test_tf_util.py new file mode 100644 index 0000000000000000000000000000000000000000..1082b1fa132ff20920246cf7440e901bf29e9aa1 --- /dev/null +++ b/baselines/common/tests/test_tf_util.py @@ -0,0 +1,42 @@ +# tests for tf_util +import tensorflow as tf +from baselines.common.tf_util import ( + function, + initialize, + single_threaded_session +) + + +def test_function(): + with tf.Graph().as_default(): + x = tf.compat.v1.placeholder(tf.int32, (), name="x") + y = tf.compat.v1.placeholder(tf.int32, (), name="y") + z = 3 * x + 2 * y + lin = function([x, y], z, givens={y: 0}) + + with single_threaded_session(): + initialize() + + assert lin(2) == 6 + assert lin(x=3) == 9 + assert lin(2, 2) == 10 + assert lin(x=2, y=3) == 12 + + +def test_multikwargs(): + with tf.Graph().as_default(): + x = tf.compat.v1.placeholder(tf.int32, (), name="x") + with tf.compat.v1.variable_scope("other"): + x2 = tf.compat.v1.placeholder(tf.int32, (), name="x") + z = 3 * x + 2 * x2 + + lin = function([x, x2], z, givens={x2: 0}) + with single_threaded_session(): + initialize() + assert lin(2) == 6 + assert lin(2, 2) == 10 + + +if __name__ == '__main__': + test_function() + test_multikwargs() diff --git a/baselines/common/tests/test_with_mpi.py b/baselines/common/tests/test_with_mpi.py new file mode 100644 index 0000000000000000000000000000000000000000..9388078feedb89d6fd9173320e04697503988797 --- /dev/null +++ b/baselines/common/tests/test_with_mpi.py @@ -0,0 +1,38 @@ +import os +import sys +import subprocess +import cloudpickle +import base64 +import pytest +from functools import wraps + +try: + from mpi4py import MPI +except ImportError: + MPI = None + +def with_mpi(nproc=2, timeout=30, skip_if_no_mpi=True): + def outer_thunk(fn): + @wraps(fn) + def thunk(*args, **kwargs): + serialized_fn = base64.b64encode(cloudpickle.dumps(lambda: fn(*args, **kwargs))) + subprocess.check_call([ + 'mpiexec','-n', str(nproc), + sys.executable, + '-m', 'baselines.common.tests.test_with_mpi', + serialized_fn + ], env=os.environ, timeout=timeout) + + if skip_if_no_mpi: + return pytest.mark.skipif(MPI is None, reason="MPI not present")(thunk) + else: + return thunk + + return outer_thunk + + +if __name__ == '__main__': + if len(sys.argv) > 1: + fn = cloudpickle.loads(base64.b64decode(sys.argv[1])) + assert callable(fn) + fn() diff --git a/baselines/common/tests/util.py b/baselines/common/tests/util.py new file mode 100644 index 0000000000000000000000000000000000000000..d621511e5d0d24cbe778262936b232b2e24b7c31 --- /dev/null +++ b/baselines/common/tests/util.py @@ -0,0 +1,92 @@ +import tensorflow as tf +import numpy as np +from baselines.common.vec_env.dummy_vec_env import DummyVecEnv + +N_TRIALS = 10000 +N_EPISODES = 100 + +_sess_config = tf.compat.v1.ConfigProto( + allow_soft_placement=True, + intra_op_parallelism_threads=1, + inter_op_parallelism_threads=1 +) + +def simple_test(env_fn, learn_fn, min_reward_fraction, n_trials=N_TRIALS): + def seeded_env_fn(): + env = env_fn() + env.seed(0) + return env + + np.random.seed(0) + env = DummyVecEnv([seeded_env_fn]) + with tf.Graph().as_default(), tf.compat.v1.Session(config=_sess_config).as_default(): + tf.compat.v1.set_random_seed(0) + model = learn_fn(env) + sum_rew = 0 + done = True + for i in range(n_trials): + if done: + obs = env.reset() + state = model.initial_state + if state is not None: + a, v, state, _ = model.step(obs, S=state, M=[False]) + else: + a, v, _, _ = model.step(obs) + obs, rew, done, _ = env.step(a) + sum_rew += float(rew) + print("Reward in {} trials is {}".format(n_trials, sum_rew)) + assert sum_rew > min_reward_fraction * n_trials, \ + 'sum of rewards {} is less than {} of the total number of trials {}'.format(sum_rew, min_reward_fraction, n_trials) + +def reward_per_episode_test(env_fn, learn_fn, min_avg_reward, n_trials=N_EPISODES): + env = DummyVecEnv([env_fn]) + with tf.Graph().as_default(), tf.compat.v1.Session(config=_sess_config).as_default(): + model = learn_fn(env) + N_TRIALS = 100 + observations, actions, rewards = rollout(env, model, N_TRIALS) + rewards = [sum(r) for r in rewards] + avg_rew = sum(rewards) / N_TRIALS + print("Average reward in {} episodes is {}".format(n_trials, avg_rew)) + assert avg_rew > min_avg_reward, \ + 'average reward in {} episodes ({}) is less than {}'.format(n_trials, avg_rew, min_avg_reward) + +def rollout(env, model, n_trials): + rewards = [] + actions = [] + observations = [] + for i in range(n_trials): + obs = env.reset() + state = model.initial_state if hasattr(model, 'initial_state') else None + episode_rew = [] + episode_actions = [] + episode_obs = [] + while True: + if state is not None: + a, v, state, _ = model.step(obs, S=state, M=[False]) + else: + a,v, _, _ = model.step(obs) + + obs, rew, done, _ = env.step(a) + episode_rew.append(rew) + episode_actions.append(a) + episode_obs.append(obs) + if done: + break + rewards.append(episode_rew) + actions.append(episode_actions) + observations.append(episode_obs) + return observations, actions, rewards + + +def smoketest(argstr, **kwargs): + import tempfile + import subprocess + import os + argstr = 'python -m baselines.run ' + argstr + for key, value in kwargs: + argstr += ' --{}={}'.format(key, value) + tempdir = tempfile.mkdtemp() + env = os.environ.copy() + env['OPENAI_LOGDIR'] = tempdir + subprocess.run(argstr.split(' '), env=env) + return tempdir diff --git a/baselines/common/tf_util.py b/baselines/common/tf_util.py new file mode 100644 index 0000000000000000000000000000000000000000..36fa8230b5665311dcc516aec3ed7de8d8c55bdd --- /dev/null +++ b/baselines/common/tf_util.py @@ -0,0 +1,443 @@ +import numpy as np +import tensorflow as tf # pylint: ignore-module +import copy +import os +import functools +import collections +import multiprocessing + +def switch(condition, then_expression, else_expression): + """Switches between two operations depending on a scalar value (int or bool). + Note that both `then_expression` and `else_expression` + should be symbolic tensors of the *same shape*. + + # Arguments + condition: scalar tensor. + then_expression: TensorFlow operation. + else_expression: TensorFlow operation. + """ + x_shape = copy.copy(then_expression.get_shape()) + x = tf.cond(pred=tf.cast(condition, 'bool'), + true_fn=lambda: then_expression, + false_fn=lambda: else_expression) + x.set_shape(x_shape) + return x + +# ================================================================ +# Extras +# ================================================================ + +def lrelu(x, leak=0.2): + f1 = 0.5 * (1 + leak) + f2 = 0.5 * (1 - leak) + return f1 * x + f2 * abs(x) + +# ================================================================ +# Mathematical utils +# ================================================================ + +def huber_loss(x, delta=1.0): + """Reference: https://en.wikipedia.org/wiki/Huber_loss""" + return tf.compat.v1.where( + tf.abs(x) < delta, + tf.square(x) * 0.5, + delta * (tf.abs(x) - 0.5 * delta) + ) + +# ================================================================ +# Global session +# ================================================================ + +def get_session(config=None): + """Get default session or create one with a given config""" + sess = tf.compat.v1.get_default_session() + if sess is None: + sess = make_session(config=config, make_default=True) + return sess + +def make_session(config=None, num_cpu=None, make_default=False, graph=None): + """Returns a session that will use CPU's only""" + if num_cpu is None: + num_cpu = int(os.getenv('RCALL_NUM_CPU', multiprocessing.cpu_count())) + if config is None: + config = tf.compat.v1.ConfigProto( + allow_soft_placement=True, + inter_op_parallelism_threads=num_cpu, + intra_op_parallelism_threads=num_cpu) + config.gpu_options.allow_growth = True + + if make_default: + return tf.compat.v1.InteractiveSession(config=config, graph=graph) + else: + return tf.compat.v1.Session(config=config, graph=graph) + +def single_threaded_session(): + """Returns a session which will only use a single CPU""" + return make_session(num_cpu=1) + +def in_session(f): + @functools.wraps(f) + def newfunc(*args, **kwargs): + with tf.compat.v1.Session(): + f(*args, **kwargs) + return newfunc + +ALREADY_INITIALIZED = set() + +def initialize(): + """Initialize all the uninitialized variables in the global scope.""" + new_variables = set(tf.compat.v1.global_variables()) - ALREADY_INITIALIZED + get_session().run(tf.compat.v1.variables_initializer(new_variables)) + ALREADY_INITIALIZED.update(new_variables) + +# ================================================================ +# Model components +# ================================================================ + +def normc_initializer(std=1.0, axis=0): + def _initializer(shape, dtype=None, partition_info=None): # pylint: disable=W0613 + out = np.random.randn(*shape).astype(dtype.as_numpy_dtype) + out *= std / np.sqrt(np.square(out).sum(axis=axis, keepdims=True)) + return tf.constant(out) + return _initializer + +def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad="SAME", dtype=tf.float32, collections=None, + summary_tag=None): + with tf.compat.v1.variable_scope(name): + stride_shape = [1, stride[0], stride[1], 1] + filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters] + + # there are "num input feature maps * filter height * filter width" + # inputs to each hidden unit + fan_in = intprod(filter_shape[:3]) + # each unit in the lower layer receives a gradient from: + # "num output feature maps * filter height * filter width" / + # pooling size + fan_out = intprod(filter_shape[:2]) * num_filters + # initialize weights with random weights + w_bound = np.sqrt(6. / (fan_in + fan_out)) + + w = tf.compat.v1.get_variable("W", filter_shape, dtype, tf.compat.v1.random_uniform_initializer(-w_bound, w_bound), + collections=collections) + b = tf.compat.v1.get_variable("b", [1, 1, 1, num_filters], initializer=tf.compat.v1.zeros_initializer(), + collections=collections) + + if summary_tag is not None: + tf.compat.v1.summary.image(summary_tag, + tf.transpose(a=tf.reshape(w, [filter_size[0], filter_size[1], -1, 1]), + perm=[2, 0, 1, 3]), + max_images=10) + + return tf.nn.conv2d(input=x, filters=w, strides=stride_shape, padding=pad) + b + +# ================================================================ +# Theano-like Function +# ================================================================ + +def function(inputs, outputs, updates=None, givens=None): + """Just like Theano function. Take a bunch of tensorflow placeholders and expressions + computed based on those placeholders and produces f(inputs) -> outputs. Function f takes + values to be fed to the input's placeholders and produces the values of the expressions + in outputs. + + Input values can be passed in the same order as inputs or can be provided as kwargs based + on placeholder name (passed to constructor or accessible via placeholder.op.name). + + Example: + x = tf.placeholder(tf.int32, (), name="x") + y = tf.placeholder(tf.int32, (), name="y") + z = 3 * x + 2 * y + lin = function([x, y], z, givens={y: 0}) + + with single_threaded_session(): + initialize() + + assert lin(2) == 6 + assert lin(x=3) == 9 + assert lin(2, 2) == 10 + assert lin(x=2, y=3) == 12 + + Parameters + ---------- + inputs: [tf.placeholder, tf.constant, or object with make_feed_dict method] + list of input arguments + outputs: [tf.Variable] or tf.Variable + list of outputs or a single output to be returned from function. Returned + value will also have the same shape. + updates: [tf.Operation] or tf.Operation + list of update functions or single update function that will be run whenever + the function is called. The return is ignored. + + """ + if isinstance(outputs, list): + return _Function(inputs, outputs, updates, givens=givens) + elif isinstance(outputs, (dict, collections.OrderedDict)): + f = _Function(inputs, outputs.values(), updates, givens=givens) + return lambda *args, **kwargs: type(outputs)(zip(outputs.keys(), f(*args, **kwargs))) + else: + f = _Function(inputs, [outputs], updates, givens=givens) + return lambda *args, **kwargs: f(*args, **kwargs)[0] + + +class _Function(object): + def __init__(self, inputs, outputs, updates, givens): + for inpt in inputs: + if not hasattr(inpt, 'make_feed_dict') and not (type(inpt) is tf.Tensor and len(inpt.op.inputs) == 0): + assert False, "inputs should all be placeholders, constants, or have a make_feed_dict method" + self.inputs = inputs + self.input_names = {inp.name.split("/")[-1].split(":")[0]: inp for inp in inputs} + updates = updates or [] + self.update_group = tf.group(*updates) + self.outputs_update = list(outputs) + [self.update_group] + self.givens = {} if givens is None else givens + + def _feed_input(self, feed_dict, inpt, value): + if hasattr(inpt, 'make_feed_dict'): + feed_dict.update(inpt.make_feed_dict(value)) + else: + feed_dict[inpt] = adjust_shape(inpt, value) + + def __call__(self, *args, **kwargs): + assert len(args) + len(kwargs) <= len(self.inputs), "Too many arguments provided" + feed_dict = {} + # Update feed dict with givens. + for inpt in self.givens: + feed_dict[inpt] = adjust_shape(inpt, feed_dict.get(inpt, self.givens[inpt])) + # Update the args + for inpt, value in zip(self.inputs, args): + self._feed_input(feed_dict, inpt, value) + for inpt_name, value in kwargs.items(): + self._feed_input(feed_dict, self.input_names[inpt_name], value) + results = get_session().run(self.outputs_update, feed_dict=feed_dict)[:-1] + return results + +# ================================================================ +# Flat vectors +# ================================================================ + +def var_shape(x): + out = x.get_shape().as_list() + assert all(isinstance(a, int) for a in out), \ + "shape function assumes that shape is fully known" + return out + +def numel(x): + return intprod(var_shape(x)) + +def intprod(x): + return int(np.prod(x)) + +def flatgrad(loss, var_list, clip_norm=None): + grads = tf.gradients(ys=loss, xs=var_list) + if clip_norm is not None: + grads = [tf.clip_by_norm(grad, clip_norm=clip_norm) for grad in grads] + return tf.concat(axis=0, values=[ + tf.reshape(grad if grad is not None else tf.zeros_like(v), [numel(v)]) + for (v, grad) in zip(var_list, grads) + ]) + +class SetFromFlat(object): + def __init__(self, var_list, dtype=tf.float32): + assigns = [] + shapes = list(map(var_shape, var_list)) + total_size = np.sum([intprod(shape) for shape in shapes]) + + self.theta = theta = tf.compat.v1.placeholder(dtype, [total_size]) + start = 0 + assigns = [] + for (shape, v) in zip(shapes, var_list): + size = intprod(shape) + assigns.append(tf.compat.v1.assign(v, tf.reshape(theta[start:start + size], shape))) + start += size + self.op = tf.group(*assigns) + + def __call__(self, theta): + tf.compat.v1.get_default_session().run(self.op, feed_dict={self.theta: theta}) + +class GetFlat(object): + def __init__(self, var_list): + self.op = tf.concat(axis=0, values=[tf.reshape(v, [numel(v)]) for v in var_list]) + + def __call__(self): + return tf.compat.v1.get_default_session().run(self.op) + +def flattenallbut0(x): + return tf.reshape(x, [-1, intprod(x.get_shape().as_list()[1:])]) + +# ============================================================= +# TF placeholders management +# ============================================================ + +_PLACEHOLDER_CACHE = {} # name -> (placeholder, dtype, shape) + +def get_placeholder(name, dtype, shape): + if name in _PLACEHOLDER_CACHE: + out, dtype1, shape1 = _PLACEHOLDER_CACHE[name] + if out.graph == tf.compat.v1.get_default_graph(): + assert dtype1 == dtype and shape1 == shape, \ + 'Placeholder with name {} has already been registered and has shape {}, different from requested {}'.format(name, shape1, shape) + return out + + out = tf.compat.v1.placeholder(dtype=dtype, shape=shape, name=name) + _PLACEHOLDER_CACHE[name] = (out, dtype, shape) + return out + +def get_placeholder_cached(name): + return _PLACEHOLDER_CACHE[name][0] + + + +# ================================================================ +# Diagnostics +# ================================================================ + +def display_var_info(vars): + from baselines import logger + count_params = 0 + for v in vars: + name = v.name + if "/Adam" in name or "beta1_power" in name or "beta2_power" in name: continue + v_params = np.prod(v.shape.as_list()) + count_params += v_params + if "/b:" in name or "/bias" in name: continue # Wx+b, bias is not interesting to look at => count params, but not print + logger.info(" %s%s %i params %s" % (name, " "*(55-len(name)), v_params, str(v.shape))) + + logger.info("Total model parameters: %0.2f million" % (count_params*1e-6)) + + +def get_available_gpus(session_config=None): + # based on recipe from https://stackoverflow.com/a/38580201 + + # Unless we allocate a session here, subsequent attempts to create one + # will ignore our custom config (in particular, allow_growth=True will have + # no effect). + if session_config is None: + session_config = get_session()._config + + from tensorflow.python.client import device_lib + local_device_protos = device_lib.list_local_devices(session_config) + return [x.name for x in local_device_protos if x.device_type == 'GPU'] + +# ================================================================ +# Saving variables +# ================================================================ + +def load_state(fname, sess=None): + from baselines import logger + logger.warn('load_state method is deprecated, please use load_variables instead') + sess = sess or get_session() + saver = tf.compat.v1.train.Saver() + saver.restore(tf.compat.v1.get_default_session(), fname) + +def save_state(fname, sess=None): + from baselines import logger + logger.warn('save_state method is deprecated, please use save_variables instead') + sess = sess or get_session() + dirname = os.path.dirname(fname) + if any(dirname): + os.makedirs(dirname, exist_ok=True) + saver = tf.compat.v1.train.Saver() + saver.save(tf.compat.v1.get_default_session(), fname) + +# The methods above and below are clearly doing the same thing, and in a rather similar way +# TODO: ensure there is no subtle differences and remove one + +def save_variables(save_path, variables=None, sess=None): + import joblib + sess = sess or get_session() + variables = variables or tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES) + + ps = sess.run(variables) + save_dict = {v.name: value for v, value in zip(variables, ps)} + dirname = os.path.dirname(save_path) + if any(dirname): + os.makedirs(dirname, exist_ok=True) + joblib.dump(save_dict, save_path) + +def load_variables(load_path, variables=None, sess=None): + import joblib + sess = sess or get_session() + variables = variables or tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES) + + loaded_params = joblib.load(os.path.expanduser(load_path)) + restores = [] + if isinstance(loaded_params, list): + assert len(loaded_params) == len(variables), 'number of variables loaded mismatches len(variables)' + for d, v in zip(loaded_params, variables): + restores.append(v.assign(d)) + else: + for v in variables: + restores.append(v.assign(loaded_params[v.name])) + + sess.run(restores) + +# ================================================================ +# Shape adjustment for feeding into tf placeholders +# ================================================================ +def adjust_shape(placeholder, data): + ''' + adjust shape of the data to the shape of the placeholder if possible. + If shape is incompatible, AssertionError is thrown + + Parameters: + placeholder tensorflow input placeholder + + data input data to be (potentially) reshaped to be fed into placeholder + + Returns: + reshaped data + ''' + + if not isinstance(data, np.ndarray) and not isinstance(data, list): + return data + if isinstance(data, list): + data = np.array(data) + + placeholder_shape = [x or -1 for x in placeholder.shape.as_list()] + + assert _check_shape(placeholder_shape, data.shape), \ + 'Shape of data {} is not compatible with shape of the placeholder {}'.format(data.shape, placeholder_shape) + + return np.reshape(data, placeholder_shape) + + +def _check_shape(placeholder_shape, data_shape): + ''' check if two shapes are compatible (i.e. differ only by dimensions of size 1, or by the batch dimension)''' + + return True + squeezed_placeholder_shape = _squeeze_shape(placeholder_shape) + squeezed_data_shape = _squeeze_shape(data_shape) + + for i, s_data in enumerate(squeezed_data_shape): + s_placeholder = squeezed_placeholder_shape[i] + if s_placeholder != -1 and s_data != s_placeholder: + return False + + return True + + +def _squeeze_shape(shape): + return [x for x in shape if x != 1] + +# ================================================================ +# Tensorboard interfacing +# ================================================================ + +def launch_tensorboard_in_background(log_dir): + ''' + To log the Tensorflow graph when using rl-algs + algorithms, you can run the following code + in your main script: + import threading, time + def start_tensorboard(session): + time.sleep(10) # Wait until graph is setup + tb_path = osp.join(logger.get_dir(), 'tb') + summary_writer = tf.summary.FileWriter(tb_path, graph=session.graph) + summary_op = tf.summary.merge_all() + launch_tensorboard_in_background(tb_path) + session = tf.get_default_session() + t = threading.Thread(target=start_tensorboard, args=([session])) + t.start() + ''' + import subprocess + subprocess.Popen(['tensorboard', '--logdir', log_dir]) diff --git a/baselines/common/tile_images.py b/baselines/common/tile_images.py new file mode 100644 index 0000000000000000000000000000000000000000..929da8994a974fbfe6065a44762fc1db3f5b36d3 --- /dev/null +++ b/baselines/common/tile_images.py @@ -0,0 +1,23 @@ +import numpy as np + +def tile_images(img_nhwc): + """ + Tile N images into one big PxQ image + (P,Q) are chosen to be as close as possible, and if N + is square, then P=Q. + + input: img_nhwc, list or array of images, ndim=4 once turned into array + n = batch index, h = height, w = width, c = channel + returns: + bigim_HWc, ndarray with ndim=3 + """ + img_nhwc = np.asarray(img_nhwc) + N, h, w, c = img_nhwc.shape + H = int(np.ceil(np.sqrt(N))) + W = int(np.ceil(float(N)/H)) + img_nhwc = np.array(list(img_nhwc) + [img_nhwc[0]*0 for _ in range(N, H*W)]) + img_HWhwc = img_nhwc.reshape(H, W, h, w, c) + img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4) + img_Hh_Ww_c = img_HhWwc.reshape(H*h, W*w, c) + return img_Hh_Ww_c + diff --git a/baselines/common/vec_env/__init__.py b/baselines/common/vec_env/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1e4ef55299b4dfc220f887f68a4c86ede30d3cdc --- /dev/null +++ b/baselines/common/vec_env/__init__.py @@ -0,0 +1,10 @@ +from .vec_env import AlreadySteppingError, NotSteppingError, VecEnv, VecEnvWrapper, VecEnvObservationWrapper, CloudpickleWrapper +from .dummy_vec_env import DummyVecEnv +from .shmem_vec_env import ShmemVecEnv +from .subproc_vec_env import SubprocVecEnv +from .vec_frame_stack import VecFrameStack +from .vec_monitor import VecMonitor +from .vec_normalize import VecNormalize +from .vec_remove_dict_obs import VecExtractDictObs + +__all__ = ['AlreadySteppingError', 'NotSteppingError', 'VecEnv', 'VecEnvWrapper', 'VecEnvObservationWrapper', 'CloudpickleWrapper', 'DummyVecEnv', 'ShmemVecEnv', 'SubprocVecEnv', 'VecFrameStack', 'VecMonitor', 'VecNormalize', 'VecExtractDictObs'] diff --git a/baselines/common/vec_env/dummy_vec_env.py b/baselines/common/vec_env/dummy_vec_env.py new file mode 100644 index 0000000000000000000000000000000000000000..adcbce806509ac7c19afd91e2f9b8b88ce032295 --- /dev/null +++ b/baselines/common/vec_env/dummy_vec_env.py @@ -0,0 +1,81 @@ +import numpy as np +from .vec_env import VecEnv +from .util import copy_obs_dict, dict_to_obs, obs_space_info + +class DummyVecEnv(VecEnv): + """ + VecEnv that does runs multiple environments sequentially, that is, + the step and reset commands are send to one environment at a time. + Useful when debugging and when num_env == 1 (in the latter case, + avoids communication overhead) + """ + def __init__(self, env_fns): + """ + Arguments: + + env_fns: iterable of callables functions that build environments + """ + self.envs = [fn() for fn in env_fns] + env = self.envs[0] + VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space) + obs_space = env.observation_space + self.keys, shapes, dtypes = obs_space_info(obs_space) + + self.buf_obs = { k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys } + self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool) + self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32) + self.buf_infos = [{} for _ in range(self.num_envs)] + self.actions = None + self.spec = self.envs[0].spec + + def step_async(self, actions): + listify = True + try: + if len(actions) == self.num_envs: + listify = False + except TypeError: + pass + + if not listify: + self.actions = actions + else: + assert self.num_envs == 1, "actions {} is either not a list or has a wrong size - cannot match to {} environments".format(actions, self.num_envs) + self.actions = [actions] + + def step_wait(self): + for e in range(self.num_envs): + action = self.actions[e] + # if isinstance(self.envs[e].action_space, spaces.Discrete): + # action = int(action) + + obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(action) + if self.buf_dones[e]: + obs = self.envs[e].reset() + self._save_obs(e, obs) + return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones), + self.buf_infos.copy()) + + def reset(self): + for e in range(self.num_envs): + obs = self.envs[e].reset() + self._save_obs(e, obs) + return self._obs_from_buf() + + def _save_obs(self, e, obs): + for k in self.keys: + if k is None: + self.buf_obs[k][e] = obs + else: + self.buf_obs[k][e] = obs[k] + + def _obs_from_buf(self): + return dict_to_obs(copy_obs_dict(self.buf_obs)) + + def get_images(self): + return [env.render(mode='rgb_array') for env in self.envs] + + def render(self, mode='human'): + if self.num_envs == 1: + return self.envs[0].render(mode=mode) + else: + return super().render(mode=mode) diff --git a/baselines/common/vec_env/shmem_vec_env.py b/baselines/common/vec_env/shmem_vec_env.py new file mode 100644 index 0000000000000000000000000000000000000000..5b9195dc57bdad795758931f7b062203919157ff --- /dev/null +++ b/baselines/common/vec_env/shmem_vec_env.py @@ -0,0 +1,141 @@ +""" +An interface for asynchronous vectorized environments. +""" + +import multiprocessing as mp +import numpy as np +from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars +import ctypes +from baselines import logger + +from .util import dict_to_obs, obs_space_info, obs_to_dict + +_NP_TO_CT = {np.float32: ctypes.c_float, + np.int32: ctypes.c_int32, + np.int8: ctypes.c_int8, + np.uint8: ctypes.c_char, + np.bool: ctypes.c_bool} + + +class ShmemVecEnv(VecEnv): + """ + Optimized version of SubprocVecEnv that uses shared variables to communicate observations. + """ + + def __init__(self, env_fns, spaces=None, context='spawn'): + """ + If you don't specify observation_space, we'll have to create a dummy + environment to get it. + """ + ctx = mp.get_context(context) + if spaces: + observation_space, action_space = spaces + else: + logger.log('Creating dummy env object to get spaces') + with logger.scoped_configure(format_strs=[]): + dummy = env_fns[0]() + observation_space, action_space = dummy.observation_space, dummy.action_space + dummy.close() + del dummy + VecEnv.__init__(self, len(env_fns), observation_space, action_space) + self.obs_keys, self.obs_shapes, self.obs_dtypes = obs_space_info(observation_space) + self.obs_bufs = [ + {k: ctx.Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys} + for _ in env_fns] + self.parent_pipes = [] + self.procs = [] + with clear_mpi_env_vars(): + for env_fn, obs_buf in zip(env_fns, self.obs_bufs): + wrapped_fn = CloudpickleWrapper(env_fn) + parent_pipe, child_pipe = ctx.Pipe() + proc = ctx.Process(target=_subproc_worker, + args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys)) + proc.daemon = True + self.procs.append(proc) + self.parent_pipes.append(parent_pipe) + proc.start() + child_pipe.close() + self.waiting_step = False + self.viewer = None + + def reset(self): + if self.waiting_step: + logger.warn('Called reset() while waiting for the step to complete') + self.step_wait() + for pipe in self.parent_pipes: + pipe.send(('reset', None)) + return self._decode_obses([pipe.recv() for pipe in self.parent_pipes]) + + def step_async(self, actions): + assert len(actions) == len(self.parent_pipes) + for pipe, act in zip(self.parent_pipes, actions): + pipe.send(('step', act)) + self.waiting_step = True + + def step_wait(self): + outs = [pipe.recv() for pipe in self.parent_pipes] + self.waiting_step = False + obs, rews, dones, infos = zip(*outs) + return self._decode_obses(obs), np.array(rews), np.array(dones), infos + + def close_extras(self): + if self.waiting_step: + self.step_wait() + for pipe in self.parent_pipes: + pipe.send(('close', None)) + for pipe in self.parent_pipes: + pipe.recv() + pipe.close() + for proc in self.procs: + proc.join() + + def get_images(self, mode='human'): + for pipe in self.parent_pipes: + pipe.send(('render', None)) + return [pipe.recv() for pipe in self.parent_pipes] + + def _decode_obses(self, obs): + result = {} + for k in self.obs_keys: + + bufs = [b[k] for b in self.obs_bufs] + o = [np.frombuffer(b.get_obj(), dtype=self.obs_dtypes[k]).reshape(self.obs_shapes[k]) for b in bufs] + result[k] = np.array(o) + return dict_to_obs(result) + + +def _subproc_worker(pipe, parent_pipe, env_fn_wrapper, obs_bufs, obs_shapes, obs_dtypes, keys): + """ + Control a single environment instance using IPC and + shared memory. + """ + def _write_obs(maybe_dict_obs): + flatdict = obs_to_dict(maybe_dict_obs) + for k in keys: + dst = obs_bufs[k].get_obj() + dst_np = np.frombuffer(dst, dtype=obs_dtypes[k]).reshape(obs_shapes[k]) # pylint: disable=W0212 + np.copyto(dst_np, flatdict[k]) + + env = env_fn_wrapper.x() + parent_pipe.close() + try: + while True: + cmd, data = pipe.recv() + if cmd == 'reset': + pipe.send(_write_obs(env.reset())) + elif cmd == 'step': + obs, reward, done, info = env.step(data) + if done: + obs = env.reset() + pipe.send((_write_obs(obs), reward, done, info)) + elif cmd == 'render': + pipe.send(env.render(mode='rgb_array')) + elif cmd == 'close': + pipe.send(None) + break + else: + raise RuntimeError('Got unrecognized cmd %s' % cmd) + except KeyboardInterrupt: + print('ShmemVecEnv worker: got KeyboardInterrupt') + finally: + env.close() diff --git a/baselines/common/vec_env/subproc_vec_env.py b/baselines/common/vec_env/subproc_vec_env.py new file mode 100644 index 0000000000000000000000000000000000000000..8c74000889c44c06a50fd0e89a8dc04d272b7793 --- /dev/null +++ b/baselines/common/vec_env/subproc_vec_env.py @@ -0,0 +1,138 @@ +import multiprocessing as mp + +import numpy as np +from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars + + +def worker(remote, parent_remote, env_fn_wrappers): + def step_env(env, action): + ob, reward, done, info = env.step(action) + if done: + ob = env.reset() + return ob, reward, done, info + + parent_remote.close() + envs = [env_fn_wrapper() for env_fn_wrapper in env_fn_wrappers.x] + try: + while True: + cmd, data = remote.recv() + if cmd == 'step': + remote.send([step_env(env, action) for env, action in zip(envs, data)]) + elif cmd == 'reset': + remote.send([env.reset() for env in envs]) + elif cmd == 'render': + remote.send([env.render(mode='rgb_array') for env in envs]) + elif cmd == 'close': + remote.close() + break + elif cmd == 'get_spaces_spec': + remote.send(CloudpickleWrapper((envs[0].observation_space, envs[0].action_space, envs[0].spec))) + else: + raise NotImplementedError + except KeyboardInterrupt: + print('SubprocVecEnv worker: got KeyboardInterrupt') + finally: + for env in envs: + env.close() + + +class SubprocVecEnv(VecEnv): + """ + VecEnv that runs multiple environments in parallel in subproceses and communicates with them via pipes. + Recommended to use when num_envs > 1 and step() can be a bottleneck. + """ + def __init__(self, env_fns, spaces=None, context='spawn', in_series=1): + """ + Arguments: + + env_fns: iterable of callables - functions that create environments to run in subprocesses. Need to be cloud-pickleable + in_series: number of environments to run in series in a single process + (e.g. when len(env_fns) == 12 and in_series == 3, it will run 4 processes, each running 3 envs in series) + """ + self.waiting = False + self.closed = False + self.in_series = in_series + nenvs = len(env_fns) + assert nenvs % in_series == 0, "Number of envs must be divisible by number of envs to run in series" + self.nremotes = nenvs // in_series + env_fns = np.array_split(env_fns, self.nremotes) + ctx = mp.get_context(context) + self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(self.nremotes)]) + self.ps = [ctx.Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn))) + for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)] + for p in self.ps: + p.daemon = True # if the main process crashes, we should not cause things to hang + with clear_mpi_env_vars(): + p.start() + for remote in self.work_remotes: + remote.close() + + self.remotes[0].send(('get_spaces_spec', None)) + observation_space, action_space, self.spec = self.remotes[0].recv().x + self.viewer = None + VecEnv.__init__(self, nenvs, observation_space, action_space) + + def step_async(self, actions): + self._assert_not_closed() + actions = np.array_split(actions, self.nremotes) + for remote, action in zip(self.remotes, actions): + remote.send(('step', action)) + self.waiting = True + + def step_wait(self): + self._assert_not_closed() + results = [remote.recv() for remote in self.remotes] + results = _flatten_list(results) + self.waiting = False + obs, rews, dones, infos = zip(*results) + return _flatten_obs(obs), np.stack(rews), np.stack(dones), infos + + def reset(self): + self._assert_not_closed() + for remote in self.remotes: + remote.send(('reset', None)) + obs = [remote.recv() for remote in self.remotes] + obs = _flatten_list(obs) + return _flatten_obs(obs) + + def close_extras(self): + self.closed = True + if self.waiting: + for remote in self.remotes: + remote.recv() + for remote in self.remotes: + remote.send(('close', None)) + for p in self.ps: + p.join() + + def get_images(self): + self._assert_not_closed() + for pipe in self.remotes: + pipe.send(('render', None)) + imgs = [pipe.recv() for pipe in self.remotes] + imgs = _flatten_list(imgs) + return imgs + + def _assert_not_closed(self): + assert not self.closed, "Trying to operate on a SubprocVecEnv after calling close()" + + def __del__(self): + if not self.closed: + self.close() + +def _flatten_obs(obs): + assert isinstance(obs, (list, tuple)) + assert len(obs) > 0 + + if isinstance(obs[0], dict): + keys = obs[0].keys() + return {k: np.stack([o[k] for o in obs]) for k in keys} + else: + return np.stack(obs) + +def _flatten_list(l): + assert isinstance(l, (list, tuple)) + assert len(l) > 0 + assert all([len(l_) > 0 for l_ in l]) + + return [l__ for l_ in l for l__ in l_] diff --git a/baselines/common/vec_env/test_vec_env.py b/baselines/common/vec_env/test_vec_env.py new file mode 100644 index 0000000000000000000000000000000000000000..e4faee354f1a7bba716b7e3b13a6a3698d776ef5 --- /dev/null +++ b/baselines/common/vec_env/test_vec_env.py @@ -0,0 +1,158 @@ +""" +Tests for asynchronous vectorized environments. +""" + +import gym +import numpy as np +import pytest +from .dummy_vec_env import DummyVecEnv +from .shmem_vec_env import ShmemVecEnv +from .subproc_vec_env import SubprocVecEnv +from baselines.common.tests.test_with_mpi import with_mpi + + +def assert_venvs_equal(venv1, venv2, num_steps): + """ + Compare two environments over num_steps steps and make sure + that the observations produced by each are the same when given + the same actions. + """ + assert venv1.num_envs == venv2.num_envs + assert venv1.observation_space.shape == venv2.observation_space.shape + assert venv1.observation_space.dtype == venv2.observation_space.dtype + assert venv1.action_space.shape == venv2.action_space.shape + assert venv1.action_space.dtype == venv2.action_space.dtype + + try: + obs1, obs2 = venv1.reset(), venv2.reset() + assert np.array(obs1).shape == np.array(obs2).shape + assert np.array(obs1).shape == (venv1.num_envs,) + venv1.observation_space.shape + assert np.allclose(obs1, obs2) + venv1.action_space.seed(1337) + for _ in range(num_steps): + actions = np.array([venv1.action_space.sample() for _ in range(venv1.num_envs)]) + for venv in [venv1, venv2]: + venv.step_async(actions) + outs1 = venv1.step_wait() + outs2 = venv2.step_wait() + for out1, out2 in zip(outs1[:3], outs2[:3]): + assert np.array(out1).shape == np.array(out2).shape + assert np.allclose(out1, out2) + assert list(outs1[3]) == list(outs2[3]) + finally: + venv1.close() + venv2.close() + + +@pytest.mark.parametrize('klass', (ShmemVecEnv, SubprocVecEnv)) +@pytest.mark.parametrize('dtype', ('uint8', 'float32')) +def test_vec_env(klass, dtype): # pylint: disable=R0914 + """ + Test that a vectorized environment is equivalent to + DummyVecEnv, since DummyVecEnv is less likely to be + error prone. + """ + num_envs = 3 + num_steps = 100 + shape = (3, 8) + + def make_fn(seed): + """ + Get an environment constructor with a seed. + """ + return lambda: SimpleEnv(seed, shape, dtype) + fns = [make_fn(i) for i in range(num_envs)] + env1 = DummyVecEnv(fns) + env2 = klass(fns) + assert_venvs_equal(env1, env2, num_steps=num_steps) + + +@pytest.mark.parametrize('dtype', ('uint8', 'float32')) +@pytest.mark.parametrize('num_envs_in_series', (3, 4, 6)) +def test_sync_sampling(dtype, num_envs_in_series): + """ + Test that a SubprocVecEnv running with envs in series + outputs the same as DummyVecEnv. + """ + num_envs = 12 + num_steps = 100 + shape = (3, 8) + + def make_fn(seed): + """ + Get an environment constructor with a seed. + """ + return lambda: SimpleEnv(seed, shape, dtype) + fns = [make_fn(i) for i in range(num_envs)] + env1 = DummyVecEnv(fns) + env2 = SubprocVecEnv(fns, in_series=num_envs_in_series) + assert_venvs_equal(env1, env2, num_steps=num_steps) + + +@pytest.mark.parametrize('dtype', ('uint8', 'float32')) +@pytest.mark.parametrize('num_envs_in_series', (3, 4, 6)) +def test_sync_sampling_sanity(dtype, num_envs_in_series): + """ + Test that a SubprocVecEnv running with envs in series + outputs the same as SubprocVecEnv without running in series. + """ + num_envs = 12 + num_steps = 100 + shape = (3, 8) + + def make_fn(seed): + """ + Get an environment constructor with a seed. + """ + return lambda: SimpleEnv(seed, shape, dtype) + fns = [make_fn(i) for i in range(num_envs)] + env1 = SubprocVecEnv(fns) + env2 = SubprocVecEnv(fns, in_series=num_envs_in_series) + assert_venvs_equal(env1, env2, num_steps=num_steps) + + +class SimpleEnv(gym.Env): + """ + An environment with a pre-determined observation space + and RNG seed. + """ + + def __init__(self, seed, shape, dtype): + np.random.seed(seed) + self._dtype = dtype + self._start_obs = np.array(np.random.randint(0, 0x100, size=shape), + dtype=dtype) + self._max_steps = seed + 1 + self._cur_obs = None + self._cur_step = 0 + # this is 0xFF instead of 0x100 because the Box space includes + # the high end, while randint does not + self.action_space = gym.spaces.Box(low=0, high=0xFF, shape=shape, dtype=dtype) + self.observation_space = self.action_space + + def step(self, action): + self._cur_obs += np.array(action, dtype=self._dtype) + self._cur_step += 1 + done = self._cur_step >= self._max_steps + reward = self._cur_step / self._max_steps + return self._cur_obs, reward, done, {'foo': 'bar' + str(reward)} + + def reset(self): + self._cur_obs = self._start_obs + self._cur_step = 0 + return self._cur_obs + + def render(self, mode=None): + raise NotImplementedError + + + +@with_mpi() +def test_mpi_with_subprocvecenv(): + shape = (2,3,4) + nenv = 1 + venv = SubprocVecEnv([lambda: SimpleEnv(0, shape, 'float32')] * nenv) + ob = venv.reset() + venv.close() + assert ob.shape == (nenv,) + shape + diff --git a/baselines/common/vec_env/test_video_recorder.py b/baselines/common/vec_env/test_video_recorder.py new file mode 100644 index 0000000000000000000000000000000000000000..363404a60f27ec0d465da9e164296a77e37ddcca --- /dev/null +++ b/baselines/common/vec_env/test_video_recorder.py @@ -0,0 +1,49 @@ +""" +Tests for asynchronous vectorized environments. +""" + +import gym +import pytest +import os +import glob +import tempfile + +from .dummy_vec_env import DummyVecEnv +from .shmem_vec_env import ShmemVecEnv +from .subproc_vec_env import SubprocVecEnv +from .vec_video_recorder import VecVideoRecorder + +@pytest.mark.parametrize('klass', (DummyVecEnv, ShmemVecEnv, SubprocVecEnv)) +@pytest.mark.parametrize('num_envs', (1, 4)) +@pytest.mark.parametrize('video_length', (10, 100)) +@pytest.mark.parametrize('video_interval', (1, 50)) +def test_video_recorder(klass, num_envs, video_length, video_interval): + """ + Wrap an existing VecEnv with VevVideoRecorder, + Make (video_interval + video_length + 1) steps, + then check that the file is present + """ + + def make_fn(): + env = gym.make('PongNoFrameskip-v4') + return env + fns = [make_fn for _ in range(num_envs)] + env = klass(fns) + + with tempfile.TemporaryDirectory() as video_path: + env = VecVideoRecorder(env, video_path, record_video_trigger=lambda x: x % video_interval == 0, video_length=video_length) + + env.reset() + for _ in range(video_interval + video_length + 1): + env.step([0] * num_envs) + env.close() + + + recorded_video = glob.glob(os.path.join(video_path, "*.mp4")) + + # first and second step + assert len(recorded_video) == 2 + # Files are not empty + assert all(os.stat(p).st_size != 0 for p in recorded_video) + + diff --git a/baselines/common/vec_env/util.py b/baselines/common/vec_env/util.py new file mode 100644 index 0000000000000000000000000000000000000000..ddc71c27e0599fe5072220bdb067ebe2c64d74d8 --- /dev/null +++ b/baselines/common/vec_env/util.py @@ -0,0 +1,62 @@ +""" +Helpers for dealing with vectorized environments. +""" + +from collections import OrderedDict + +import gym +import numpy as np + + +def copy_obs_dict(obs): + """ + Deep-copy an observation dict. + """ + return {k: np.copy(v) for k, v in obs.items()} + + +def dict_to_obs(obs_dict): + """ + Convert an observation dict into a raw array if the + original observation space was not a Dict space. + """ + if set(obs_dict.keys()) == {None}: + return obs_dict[None] + return obs_dict + + +def obs_space_info(obs_space): + """ + Get dict-structured information about a gym.Space. + + Returns: + A tuple (keys, shapes, dtypes): + keys: a list of dict keys. + shapes: a dict mapping keys to shapes. + dtypes: a dict mapping keys to dtypes. + """ + if isinstance(obs_space, gym.spaces.Dict): + assert isinstance(obs_space.spaces, OrderedDict) + subspaces = obs_space.spaces + elif isinstance(obs_space, gym.spaces.Tuple): + assert isinstance(obs_space.spaces, tuple) + subspaces = {i: obs_space.spaces[i] for i in range(len(obs_space.spaces))} + else: + subspaces = {None: obs_space} + keys = [] + shapes = {} + dtypes = {} + for key, box in subspaces.items(): + keys.append(key) + shapes[key] = box.shape + dtypes[key] = box.dtype + return keys, shapes, dtypes + + +def obs_to_dict(obs): + """ + Convert an observation into a dict. + """ + if isinstance(obs, dict): + return obs + return {None: obs} diff --git a/baselines/common/vec_env/vec_env.py b/baselines/common/vec_env/vec_env.py new file mode 100644 index 0000000000000000000000000000000000000000..fc6098e2ecd232f720cd029a2e9d1c977f810be4 --- /dev/null +++ b/baselines/common/vec_env/vec_env.py @@ -0,0 +1,223 @@ +import contextlib +import os +from abc import ABC, abstractmethod + +from baselines.common.tile_images import tile_images + +class AlreadySteppingError(Exception): + """ + Raised when an asynchronous step is running while + step_async() is called again. + """ + + def __init__(self): + msg = 'already running an async step' + Exception.__init__(self, msg) + + +class NotSteppingError(Exception): + """ + Raised when an asynchronous step is not running but + step_wait() is called. + """ + + def __init__(self): + msg = 'not running an async step' + Exception.__init__(self, msg) + + +class VecEnv(ABC): + """ + An abstract asynchronous, vectorized environment. + Used to batch data from multiple copies of an environment, so that + each observation becomes an batch of observations, and expected action is a batch of actions to + be applied per-environment. + """ + closed = False + viewer = None + + metadata = { + 'render.modes': ['human', 'rgb_array'] + } + + def __init__(self, num_envs, observation_space, action_space): + self.num_envs = num_envs + self.observation_space = observation_space + self.action_space = action_space + + @abstractmethod + def reset(self): + """ + Reset all the environments and return an array of + observations, or a dict of observation arrays. + + If step_async is still doing work, that work will + be cancelled and step_wait() should not be called + until step_async() is invoked again. + """ + pass + + @abstractmethod + def step_async(self, actions): + """ + Tell all the environments to start taking a step + with the given actions. + Call step_wait() to get the results of the step. + + You should not call this if a step_async run is + already pending. + """ + pass + + @abstractmethod + def step_wait(self): + """ + Wait for the step taken with step_async(). + + Returns (obs, rews, dones, infos): + - obs: an array of observations, or a dict of + arrays of observations. + - rews: an array of rewards + - dones: an array of "episode done" booleans + - infos: a sequence of info objects + """ + pass + + def close_extras(self): + """ + Clean up the extra resources, beyond what's in this base class. + Only runs when not self.closed. + """ + pass + + def close(self): + if self.closed: + return + if self.viewer is not None: + self.viewer.close() + self.close_extras() + self.closed = True + + def step(self, actions): + """ + Step the environments synchronously. + + This is available for backwards compatibility. + """ + self.step_async(actions) + return self.step_wait() + + def render(self, mode='human'): + imgs = self.get_images() + bigimg = tile_images(imgs) + if mode == 'human': + self.get_viewer().imshow(bigimg) + return self.get_viewer().isopen + elif mode == 'rgb_array': + return bigimg + else: + raise NotImplementedError + + def get_images(self): + """ + Return RGB images from each environment + """ + raise NotImplementedError + + @property + def unwrapped(self): + if isinstance(self, VecEnvWrapper): + return self.venv.unwrapped + else: + return self + + def get_viewer(self): + if self.viewer is None: + from gym.envs.classic_control import rendering + self.viewer = rendering.SimpleImageViewer() + return self.viewer + +class VecEnvWrapper(VecEnv): + """ + An environment wrapper that applies to an entire batch + of environments at once. + """ + + def __init__(self, venv, observation_space=None, action_space=None): + self.venv = venv + super().__init__(num_envs=venv.num_envs, + observation_space=observation_space or venv.observation_space, + action_space=action_space or venv.action_space) + + def step_async(self, actions): + self.venv.step_async(actions) + + @abstractmethod + def reset(self): + pass + + @abstractmethod + def step_wait(self): + pass + + def close(self): + return self.venv.close() + + def render(self, mode='human'): + return self.venv.render(mode=mode) + + def get_images(self): + return self.venv.get_images() + + def __getattr__(self, name): + if name.startswith('_'): + raise AttributeError("attempted to get missing private attribute '{}'".format(name)) + return getattr(self.venv, name) + +class VecEnvObservationWrapper(VecEnvWrapper): + @abstractmethod + def process(self, obs): + pass + + def reset(self): + obs = self.venv.reset() + return self.process(obs) + + def step_wait(self): + obs, rews, dones, infos = self.venv.step_wait() + return self.process(obs), rews, dones, infos + +class CloudpickleWrapper(object): + """ + Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle) + """ + + def __init__(self, x): + self.x = x + + def __getstate__(self): + import cloudpickle + return cloudpickle.dumps(self.x) + + def __setstate__(self, ob): + import pickle + self.x = pickle.loads(ob) + + +@contextlib.contextmanager +def clear_mpi_env_vars(): + """ + from mpi4py import MPI will call MPI_Init by default. If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang. + This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing + Processes. + """ + removed_environment = {} + for k, v in list(os.environ.items()): + for prefix in ['OMPI_', 'PMI_']: + if k.startswith(prefix): + removed_environment[k] = v + del os.environ[k] + try: + yield + finally: + os.environ.update(removed_environment) diff --git a/baselines/common/vec_env/vec_frame_stack.py b/baselines/common/vec_env/vec_frame_stack.py new file mode 100644 index 0000000000000000000000000000000000000000..0da1a0aadacccadfb2344ab369c3202212dd0c9b --- /dev/null +++ b/baselines/common/vec_env/vec_frame_stack.py @@ -0,0 +1,30 @@ +from .vec_env import VecEnvWrapper +import numpy as np +from gym import spaces + + +class VecFrameStack(VecEnvWrapper): + def __init__(self, venv, nstack): + self.venv = venv + self.nstack = nstack + wos = venv.observation_space # wrapped ob space + low = np.repeat(wos.low, self.nstack, axis=-1) + high = np.repeat(wos.high, self.nstack, axis=-1) + self.stackedobs = np.zeros((venv.num_envs,) + low.shape, low.dtype) + observation_space = spaces.Box(low=low, high=high, dtype=venv.observation_space.dtype) + VecEnvWrapper.__init__(self, venv, observation_space=observation_space) + + def step_wait(self): + obs, rews, news, infos = self.venv.step_wait() + self.stackedobs = np.roll(self.stackedobs, shift=-1, axis=-1) + for (i, new) in enumerate(news): + if new: + self.stackedobs[i] = 0 + self.stackedobs[..., -obs.shape[-1]:] = obs + return self.stackedobs, rews, news, infos + + def reset(self): + obs = self.venv.reset() + self.stackedobs[...] = 0 + self.stackedobs[..., -obs.shape[-1]:] = obs + return self.stackedobs diff --git a/baselines/common/vec_env/vec_monitor.py b/baselines/common/vec_env/vec_monitor.py new file mode 100644 index 0000000000000000000000000000000000000000..efaafc9c7385d6e01d414e55270561c9fdb59a38 --- /dev/null +++ b/baselines/common/vec_env/vec_monitor.py @@ -0,0 +1,55 @@ +from . import VecEnvWrapper +from baselines.bench.monitor import ResultsWriter +import numpy as np +import time +from collections import deque + +class VecMonitor(VecEnvWrapper): + def __init__(self, venv, filename=None, keep_buf=0, info_keywords=()): + VecEnvWrapper.__init__(self, venv) + self.eprets = None + self.eplens = None + self.epcount = 0 + self.tstart = time.time() + if filename: + self.results_writer = ResultsWriter(filename, header={'t_start': self.tstart}, + extra_keys=info_keywords) + else: + self.results_writer = None + self.info_keywords = info_keywords + self.keep_buf = keep_buf + if self.keep_buf: + self.epret_buf = deque([], maxlen=keep_buf) + self.eplen_buf = deque([], maxlen=keep_buf) + + def reset(self): + obs = self.venv.reset() + self.eprets = np.zeros(self.num_envs, 'f') + self.eplens = np.zeros(self.num_envs, 'i') + return obs + + def step_wait(self): + obs, rews, dones, infos = self.venv.step_wait() + self.eprets += rews + self.eplens += 1 + + newinfos = list(infos[:]) + for i in range(len(dones)): + if dones[i]: + info = infos[i].copy() + ret = self.eprets[i] + eplen = self.eplens[i] + epinfo = {'r': ret, 'l': eplen, 't': round(time.time() - self.tstart, 6)} + for k in self.info_keywords: + epinfo[k] = info[k] + info['episode'] = epinfo + if self.keep_buf: + self.epret_buf.append(ret) + self.eplen_buf.append(eplen) + self.epcount += 1 + self.eprets[i] = 0 + self.eplens[i] = 0 + if self.results_writer: + self.results_writer.write_row(epinfo) + newinfos[i] = info + return obs, rews, dones, newinfos diff --git a/baselines/common/vec_env/vec_normalize.py b/baselines/common/vec_env/vec_normalize.py new file mode 100644 index 0000000000000000000000000000000000000000..51a45150c860724465245c3cc8b94cb33eecea1b --- /dev/null +++ b/baselines/common/vec_env/vec_normalize.py @@ -0,0 +1,47 @@ +from . import VecEnvWrapper +import numpy as np + +class VecNormalize(VecEnvWrapper): + """ + A vectorized wrapper that normalizes the observations + and returns from an environment. + """ + + def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8, use_tf=False): + VecEnvWrapper.__init__(self, venv) + if use_tf: + from baselines.common.running_mean_std import TfRunningMeanStd + self.ob_rms = TfRunningMeanStd(shape=self.observation_space.shape, scope='ob_rms') if ob else None + self.ret_rms = TfRunningMeanStd(shape=(), scope='ret_rms') if ret else None + else: + from baselines.common.running_mean_std import RunningMeanStd + self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None + self.ret_rms = RunningMeanStd(shape=()) if ret else None + self.clipob = clipob + self.cliprew = cliprew + self.ret = np.zeros(self.num_envs) + self.gamma = gamma + self.epsilon = epsilon + + def step_wait(self): + obs, rews, news, infos = self.venv.step_wait() + self.ret = self.ret * self.gamma + rews + obs = self._obfilt(obs) + if self.ret_rms: + self.ret_rms.update(self.ret) + rews = np.clip(rews / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew) + self.ret[news] = 0. + return obs, rews, news, infos + + def _obfilt(self, obs): + if self.ob_rms: + self.ob_rms.update(obs) + obs = np.clip((obs - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob, self.clipob) + return obs + else: + return obs + + def reset(self): + self.ret = np.zeros(self.num_envs) + obs = self.venv.reset() + return self._obfilt(obs) diff --git a/baselines/common/vec_env/vec_remove_dict_obs.py b/baselines/common/vec_env/vec_remove_dict_obs.py new file mode 100644 index 0000000000000000000000000000000000000000..a6c465692d15ebddf9beabd876867a62d8345f06 --- /dev/null +++ b/baselines/common/vec_env/vec_remove_dict_obs.py @@ -0,0 +1,10 @@ +from .vec_env import VecEnvObservationWrapper + +class VecExtractDictObs(VecEnvObservationWrapper): + def __init__(self, venv, key): + self.key = key + super().__init__(venv=venv, + observation_space=venv.observation_space.spaces[self.key]) + + def process(self, obs): + return obs[self.key] diff --git a/baselines/common/vec_env/vec_video_recorder.py b/baselines/common/vec_env/vec_video_recorder.py new file mode 100644 index 0000000000000000000000000000000000000000..b4e7059ff0a7d20710482d1aa8262fbcc2128671 --- /dev/null +++ b/baselines/common/vec_env/vec_video_recorder.py @@ -0,0 +1,89 @@ +import os +from baselines import logger +from baselines.common.vec_env import VecEnvWrapper +from gym.wrappers.monitoring import video_recorder + + +class VecVideoRecorder(VecEnvWrapper): + """ + Wrap VecEnv to record rendered image as mp4 video. + """ + + def __init__(self, venv, directory, record_video_trigger, video_length=200): + """ + # Arguments + venv: VecEnv to wrap + directory: Where to save videos + record_video_trigger: + Function that defines when to start recording. + The function takes the current number of step, + and returns whether we should start recording or not. + video_length: Length of recorded video + """ + + VecEnvWrapper.__init__(self, venv) + self.record_video_trigger = record_video_trigger + self.video_recorder = None + + self.directory = os.path.abspath(directory) + if not os.path.exists(self.directory): os.mkdir(self.directory) + + self.file_prefix = "vecenv" + self.file_infix = '{}'.format(os.getpid()) + self.step_id = 0 + self.video_length = video_length + + self.recording = False + self.recorded_frames = 0 + + def reset(self): + obs = self.venv.reset() + + self.start_video_recorder() + + return obs + + def start_video_recorder(self): + self.close_video_recorder() + + base_path = os.path.join(self.directory, '{}.video.{}.video{:06}'.format(self.file_prefix, self.file_infix, self.step_id)) + self.video_recorder = video_recorder.VideoRecorder( + env=self.venv, + base_path=base_path, + metadata={'step_id': self.step_id} + ) + + self.video_recorder.capture_frame() + self.recorded_frames = 1 + self.recording = True + + def _video_enabled(self): + return self.record_video_trigger(self.step_id) + + def step_wait(self): + obs, rews, dones, infos = self.venv.step_wait() + + self.step_id += 1 + if self.recording: + self.video_recorder.capture_frame() + self.recorded_frames += 1 + if self.recorded_frames > self.video_length: + logger.info("Saving video to ", self.video_recorder.path) + self.close_video_recorder() + elif self._video_enabled(): + self.start_video_recorder() + + return obs, rews, dones, infos + + def close_video_recorder(self): + if self.recording: + self.video_recorder.close() + self.recording = False + self.recorded_frames = 0 + + def close(self): + VecEnvWrapper.close(self) + self.close_video_recorder() + + def __del__(self): + self.close() diff --git a/baselines/common/wrappers.py b/baselines/common/wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..e5e93b057d5385e8f04006d1c1a1e4aeb2da2b36 --- /dev/null +++ b/baselines/common/wrappers.py @@ -0,0 +1,29 @@ +import gym + +class TimeLimit(gym.Wrapper): + def __init__(self, env, max_episode_steps=None): + super(TimeLimit, self).__init__(env) + self._max_episode_steps = max_episode_steps + self._elapsed_steps = 0 + + def step(self, ac): + observation, reward, done, info = self.env.step(ac) + self._elapsed_steps += 1 + if self._elapsed_steps >= self._max_episode_steps: + done = True + info['TimeLimit.truncated'] = True + return observation, reward, done, info + + def reset(self, **kwargs): + self._elapsed_steps = 0 + return self.env.reset(**kwargs) + +class ClipActionsWrapper(gym.Wrapper): + def step(self, action): + import numpy as np + action = np.nan_to_num(action) + action = np.clip(action, self.action_space.low, self.action_space.high) + return self.env.step(action) + + def reset(self, **kwargs): + return self.env.reset(**kwargs) diff --git a/baselines/ddpg/README.md b/baselines/ddpg/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ed6d23fa610769665ed5d9f8116f2f5b74dfb9a9 --- /dev/null +++ b/baselines/ddpg/README.md @@ -0,0 +1,5 @@ +# DDPG + +- Original paper: https://arxiv.org/abs/1509.02971 +- Baselines post: https://blog.openai.com/better-exploration-with-parameter-noise/ +- `python -m baselines.run --alg=ddpg --env=HalfCheetah-v2 --num_timesteps=1e6` runs the algorithm for 1M frames = 10M timesteps on a Mujoco environment. See help (`-h`) for more options. diff --git a/baselines/ddpg/__init__.py b/baselines/ddpg/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/ddpg/ddpg.py b/baselines/ddpg/ddpg.py new file mode 100644 index 0000000000000000000000000000000000000000..dd53ce1509e6759c29963c7b1ea8d516ab3fc5e5 --- /dev/null +++ b/baselines/ddpg/ddpg.py @@ -0,0 +1,275 @@ +import os +import time +from collections import deque +import pickle + +from baselines.ddpg.ddpg_learner import DDPG +from baselines.ddpg.models import Actor, Critic +from baselines.ddpg.memory import Memory +from baselines.ddpg.noise import AdaptiveParamNoiseSpec, NormalActionNoise, OrnsteinUhlenbeckActionNoise +from baselines.common import set_global_seeds +import baselines.common.tf_util as U + +from baselines import logger +import numpy as np + +try: + from mpi4py import MPI +except ImportError: + MPI = None + +def learn(network, env, + seed=None, + total_timesteps=None, + nb_epochs=None, # with default settings, perform 1M steps total + nb_epoch_cycles=20, + nb_rollout_steps=100, + reward_scale=1.0, + render=False, + render_eval=False, + noise_type='adaptive-param_0.2', + normalize_returns=False, + normalize_observations=True, + critic_l2_reg=1e-2, + actor_lr=1e-4, + critic_lr=1e-3, + popart=False, + gamma=0.99, + clip_norm=None, + nb_train_steps=50, # per epoch cycle and MPI worker, + nb_eval_steps=100, + batch_size=64, # per MPI worker + tau=0.01, + eval_env=None, + param_noise_adaption_interval=50, + **network_kwargs): + + set_global_seeds(seed) + + if total_timesteps is not None: + assert nb_epochs is None + nb_epochs = int(total_timesteps) // (nb_epoch_cycles * nb_rollout_steps) + else: + nb_epochs = 500 + + if MPI is not None: + rank = MPI.COMM_WORLD.Get_rank() + else: + rank = 0 + + nb_actions = env.action_space.shape[-1] + assert (np.abs(env.action_space.low) == env.action_space.high).all() # we assume symmetric actions. + + memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape) + critic = Critic(network=network, **network_kwargs) + actor = Actor(nb_actions, network=network, **network_kwargs) + + action_noise = None + param_noise = None + if noise_type is not None: + for current_noise_type in noise_type.split(','): + current_noise_type = current_noise_type.strip() + if current_noise_type == 'none': + pass + elif 'adaptive-param' in current_noise_type: + _, stddev = current_noise_type.split('_') + param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev)) + elif 'normal' in current_noise_type: + _, stddev = current_noise_type.split('_') + action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions)) + elif 'ou' in current_noise_type: + _, stddev = current_noise_type.split('_') + action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions)) + else: + raise RuntimeError('unknown noise type "{}"'.format(current_noise_type)) + + max_action = env.action_space.high + logger.info('scaling actions by {} before executing in env'.format(max_action)) + + agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape, + gamma=gamma, tau=tau, normalize_returns=normalize_returns, normalize_observations=normalize_observations, + batch_size=batch_size, action_noise=action_noise, param_noise=param_noise, critic_l2_reg=critic_l2_reg, + actor_lr=actor_lr, critic_lr=critic_lr, enable_popart=popart, clip_norm=clip_norm, + reward_scale=reward_scale) + logger.info('Using agent with the following configuration:') + logger.info(str(agent.__dict__.items())) + + eval_episode_rewards_history = deque(maxlen=100) + episode_rewards_history = deque(maxlen=100) + sess = U.get_session() + # Prepare everything. + agent.initialize(sess) + sess.graph.finalize() + + agent.reset() + + obs = env.reset() + if eval_env is not None: + eval_obs = eval_env.reset() + nenvs = obs.shape[0] + + episode_reward = np.zeros(nenvs, dtype = np.float32) #vector + episode_step = np.zeros(nenvs, dtype = int) # vector + episodes = 0 #scalar + t = 0 # scalar + + epoch = 0 + + + + start_time = time.time() + + epoch_episode_rewards = [] + epoch_episode_steps = [] + epoch_actions = [] + epoch_qs = [] + epoch_episodes = 0 + for epoch in range(nb_epochs): + for cycle in range(nb_epoch_cycles): + # Perform rollouts. + if nenvs > 1: + # if simulating multiple envs in parallel, impossible to reset agent at the end of the episode in each + # of the environments, so resetting here instead + agent.reset() + for t_rollout in range(nb_rollout_steps): + # Predict next action. + action, q, _, _ = agent.step(obs, apply_noise=True, compute_Q=True) + + # Execute next action. + if rank == 0 and render: + env.render() + + # max_action is of dimension A, whereas action is dimension (nenvs, A) - the multiplication gets broadcasted to the batch + new_obs, r, done, info = env.step(max_action * action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1]) + # note these outputs are batched from vecenv + + t += 1 + if rank == 0 and render: + env.render() + episode_reward += r + episode_step += 1 + + # Book-keeping. + epoch_actions.append(action) + epoch_qs.append(q) + agent.store_transition(obs, action, r, new_obs, done) #the batched data will be unrolled in memory.py's append. + + obs = new_obs + + for d in range(len(done)): + if done[d]: + # Episode done. + epoch_episode_rewards.append(episode_reward[d]) + episode_rewards_history.append(episode_reward[d]) + epoch_episode_steps.append(episode_step[d]) + episode_reward[d] = 0. + episode_step[d] = 0 + epoch_episodes += 1 + episodes += 1 + if nenvs == 1: + agent.reset() + + + + # Train. + epoch_actor_losses = [] + epoch_critic_losses = [] + epoch_adaptive_distances = [] + for t_train in range(nb_train_steps): + # Adapt param noise, if necessary. + if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0: + distance = agent.adapt_param_noise() + epoch_adaptive_distances.append(distance) + + cl, al = agent.train() + epoch_critic_losses.append(cl) + epoch_actor_losses.append(al) + agent.update_target_net() + + # Evaluate. + eval_episode_rewards = [] + eval_qs = [] + if eval_env is not None: + nenvs_eval = eval_obs.shape[0] + eval_episode_reward = np.zeros(nenvs_eval, dtype = np.float32) + for t_rollout in range(nb_eval_steps): + eval_action, eval_q, _, _ = agent.step(eval_obs, apply_noise=False, compute_Q=True) + eval_obs, eval_r, eval_done, eval_info = eval_env.step(max_action * eval_action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1]) + if render_eval: + eval_env.render() + eval_episode_reward += eval_r + + eval_qs.append(eval_q) + for d in range(len(eval_done)): + if eval_done[d]: + eval_episode_rewards.append(eval_episode_reward[d]) + eval_episode_rewards_history.append(eval_episode_reward[d]) + eval_episode_reward[d] = 0.0 + + if MPI is not None: + mpi_size = MPI.COMM_WORLD.Get_size() + else: + mpi_size = 1 + + # Log stats. + # XXX shouldn't call np.mean on variable length lists + duration = time.time() - start_time + stats = agent.get_stats() + combined_stats = stats.copy() + combined_stats['rollout/return'] = np.mean(epoch_episode_rewards) + combined_stats['rollout/return_std'] = np.std(epoch_episode_rewards) + combined_stats['rollout/return_history'] = np.mean(episode_rewards_history) + combined_stats['rollout/return_history_std'] = np.std(episode_rewards_history) + combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps) + combined_stats['rollout/actions_mean'] = np.mean(epoch_actions) + combined_stats['rollout/Q_mean'] = np.mean(epoch_qs) + combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses) + combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses) + combined_stats['train/param_noise_distance'] = np.mean(epoch_adaptive_distances) + combined_stats['total/duration'] = duration + combined_stats['total/steps_per_second'] = float(t) / float(duration) + combined_stats['total/episodes'] = episodes + combined_stats['rollout/episodes'] = epoch_episodes + combined_stats['rollout/actions_std'] = np.std(epoch_actions) + # Evaluation statistics. + if eval_env is not None: + combined_stats['eval/return'] = eval_episode_rewards + combined_stats['eval/return_history'] = np.mean(eval_episode_rewards_history) + combined_stats['eval/Q'] = eval_qs + combined_stats['eval/episodes'] = len(eval_episode_rewards) + def as_scalar(x): + if isinstance(x, np.ndarray): + assert x.size == 1 + return x[0] + elif np.isscalar(x): + return x + else: + raise ValueError('expected scalar, got %s'%x) + + combined_stats_sums = np.array([ np.array(x).flatten()[0] for x in combined_stats.values()]) + if MPI is not None: + combined_stats_sums = MPI.COMM_WORLD.allreduce(combined_stats_sums) + + combined_stats = {k : v / mpi_size for (k,v) in zip(combined_stats.keys(), combined_stats_sums)} + + # Total statistics. + combined_stats['total/epochs'] = epoch + 1 + combined_stats['total/steps'] = t + + for key in sorted(combined_stats.keys()): + logger.record_tabular(key, combined_stats[key]) + + if rank == 0: + logger.dump_tabular() + logger.info('') + logdir = logger.get_dir() + if rank == 0 and logdir: + if hasattr(env, 'get_state'): + with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f: + pickle.dump(env.get_state(), f) + if eval_env and hasattr(eval_env, 'get_state'): + with open(os.path.join(logdir, 'eval_env_state.pkl'), 'wb') as f: + pickle.dump(eval_env.get_state(), f) + + + return agent diff --git a/baselines/ddpg/ddpg_learner.py b/baselines/ddpg/ddpg_learner.py new file mode 100644 index 0000000000000000000000000000000000000000..4c1f276b08b7ece17b1e5bd79359d039698c3da0 --- /dev/null +++ b/baselines/ddpg/ddpg_learner.py @@ -0,0 +1,396 @@ +from copy import copy +from functools import reduce + +import numpy as np +import tensorflow as tf +import tensorflow.contrib as tc + +from baselines import logger +from baselines.common.mpi_adam import MpiAdam +import baselines.common.tf_util as U +from baselines.common.mpi_running_mean_std import RunningMeanStd +try: + from mpi4py import MPI +except ImportError: + MPI = None + +def normalize(x, stats): + if stats is None: + return x + return (x - stats.mean) / (stats.std + 1e-8) + + +def denormalize(x, stats): + if stats is None: + return x + return x * stats.std + stats.mean + +def reduce_std(x, axis=None, keepdims=False): + return tf.sqrt(reduce_var(x, axis=axis, keepdims=keepdims)) + +def reduce_var(x, axis=None, keepdims=False): + m = tf.reduce_mean(input_tensor=x, axis=axis, keepdims=True) + devs_squared = tf.square(x - m) + return tf.reduce_mean(input_tensor=devs_squared, axis=axis, keepdims=keepdims) + +def get_target_updates(vars, target_vars, tau): + logger.info('setting up target updates ...') + soft_updates = [] + init_updates = [] + assert len(vars) == len(target_vars) + for var, target_var in zip(vars, target_vars): + logger.info(' {} <- {}'.format(target_var.name, var.name)) + init_updates.append(tf.compat.v1.assign(target_var, var)) + soft_updates.append(tf.compat.v1.assign(target_var, (1. - tau) * target_var + tau * var)) + assert len(init_updates) == len(vars) + assert len(soft_updates) == len(vars) + return tf.group(*init_updates), tf.group(*soft_updates) + + +def get_perturbed_actor_updates(actor, perturbed_actor, param_noise_stddev): + assert len(actor.vars) == len(perturbed_actor.vars) + assert len(actor.perturbable_vars) == len(perturbed_actor.perturbable_vars) + + updates = [] + for var, perturbed_var in zip(actor.vars, perturbed_actor.vars): + if var in actor.perturbable_vars: + logger.info(' {} <- {} + noise'.format(perturbed_var.name, var.name)) + updates.append(tf.compat.v1.assign(perturbed_var, var + tf.random.normal(tf.shape(input=var), mean=0., stddev=param_noise_stddev))) + else: + logger.info(' {} <- {}'.format(perturbed_var.name, var.name)) + updates.append(tf.compat.v1.assign(perturbed_var, var)) + assert len(updates) == len(actor.vars) + return tf.group(*updates) + + +class DDPG(object): + def __init__(self, actor, critic, memory, observation_shape, action_shape, param_noise=None, action_noise=None, + gamma=0.99, tau=0.001, normalize_returns=False, enable_popart=False, normalize_observations=True, + batch_size=128, observation_range=(-5., 5.), action_range=(-1., 1.), return_range=(-np.inf, np.inf), + critic_l2_reg=0., actor_lr=1e-4, critic_lr=1e-3, clip_norm=None, reward_scale=1.): + # Inputs. + self.obs0 = tf.compat.v1.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs0') + self.obs1 = tf.compat.v1.placeholder(tf.float32, shape=(None,) + observation_shape, name='obs1') + self.terminals1 = tf.compat.v1.placeholder(tf.float32, shape=(None, 1), name='terminals1') + self.rewards = tf.compat.v1.placeholder(tf.float32, shape=(None, 1), name='rewards') + self.actions = tf.compat.v1.placeholder(tf.float32, shape=(None,) + action_shape, name='actions') + self.critic_target = tf.compat.v1.placeholder(tf.float32, shape=(None, 1), name='critic_target') + self.param_noise_stddev = tf.compat.v1.placeholder(tf.float32, shape=(), name='param_noise_stddev') + + # Parameters. + self.gamma = gamma + self.tau = tau + self.memory = memory + self.normalize_observations = normalize_observations + self.normalize_returns = normalize_returns + self.action_noise = action_noise + self.param_noise = param_noise + self.action_range = action_range + self.return_range = return_range + self.observation_range = observation_range + self.critic = critic + self.actor = actor + self.actor_lr = actor_lr + self.critic_lr = critic_lr + self.clip_norm = clip_norm + self.enable_popart = enable_popart + self.reward_scale = reward_scale + self.batch_size = batch_size + self.stats_sample = None + self.critic_l2_reg = critic_l2_reg + + # Observation normalization. + if self.normalize_observations: + with tf.compat.v1.variable_scope('obs_rms'): + self.obs_rms = RunningMeanStd(shape=observation_shape) + else: + self.obs_rms = None + normalized_obs0 = tf.clip_by_value(normalize(self.obs0, self.obs_rms), + self.observation_range[0], self.observation_range[1]) + normalized_obs1 = tf.clip_by_value(normalize(self.obs1, self.obs_rms), + self.observation_range[0], self.observation_range[1]) + + # Return normalization. + if self.normalize_returns: + with tf.compat.v1.variable_scope('ret_rms'): + self.ret_rms = RunningMeanStd() + else: + self.ret_rms = None + + # Create target networks. + target_actor = copy(actor) + target_actor.name = 'target_actor' + self.target_actor = target_actor + target_critic = copy(critic) + target_critic.name = 'target_critic' + self.target_critic = target_critic + + # Create networks and core TF parts that are shared across setup parts. + self.actor_tf = actor(normalized_obs0) + self.normalized_critic_tf = critic(normalized_obs0, self.actions) + self.critic_tf = denormalize(tf.clip_by_value(self.normalized_critic_tf, self.return_range[0], self.return_range[1]), self.ret_rms) + self.normalized_critic_with_actor_tf = critic(normalized_obs0, self.actor_tf, reuse=True) + self.critic_with_actor_tf = denormalize(tf.clip_by_value(self.normalized_critic_with_actor_tf, self.return_range[0], self.return_range[1]), self.ret_rms) + Q_obs1 = denormalize(target_critic(normalized_obs1, target_actor(normalized_obs1)), self.ret_rms) + self.target_Q = self.rewards + (1. - self.terminals1) * gamma * Q_obs1 + + # Set up parts. + if self.param_noise is not None: + self.setup_param_noise(normalized_obs0) + self.setup_actor_optimizer() + self.setup_critic_optimizer() + if self.normalize_returns and self.enable_popart: + self.setup_popart() + self.setup_stats() + self.setup_target_network_updates() + + self.initial_state = None # recurrent architectures not supported yet + + def setup_target_network_updates(self): + actor_init_updates, actor_soft_updates = get_target_updates(self.actor.vars, self.target_actor.vars, self.tau) + critic_init_updates, critic_soft_updates = get_target_updates(self.critic.vars, self.target_critic.vars, self.tau) + self.target_init_updates = [actor_init_updates, critic_init_updates] + self.target_soft_updates = [actor_soft_updates, critic_soft_updates] + + def setup_param_noise(self, normalized_obs0): + assert self.param_noise is not None + + # Configure perturbed actor. + param_noise_actor = copy(self.actor) + param_noise_actor.name = 'param_noise_actor' + self.perturbed_actor_tf = param_noise_actor(normalized_obs0) + logger.info('setting up param noise') + self.perturb_policy_ops = get_perturbed_actor_updates(self.actor, param_noise_actor, self.param_noise_stddev) + + # Configure separate copy for stddev adoption. + adaptive_param_noise_actor = copy(self.actor) + adaptive_param_noise_actor.name = 'adaptive_param_noise_actor' + adaptive_actor_tf = adaptive_param_noise_actor(normalized_obs0) + self.perturb_adaptive_policy_ops = get_perturbed_actor_updates(self.actor, adaptive_param_noise_actor, self.param_noise_stddev) + self.adaptive_policy_distance = tf.sqrt(tf.reduce_mean(input_tensor=tf.square(self.actor_tf - adaptive_actor_tf))) + + def setup_actor_optimizer(self): + logger.info('setting up actor optimizer') + self.actor_loss = -tf.reduce_mean(input_tensor=self.critic_with_actor_tf) + actor_shapes = [var.get_shape().as_list() for var in self.actor.trainable_vars] + actor_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in actor_shapes]) + logger.info(' actor shapes: {}'.format(actor_shapes)) + logger.info(' actor params: {}'.format(actor_nb_params)) + self.actor_grads = U.flatgrad(self.actor_loss, self.actor.trainable_vars, clip_norm=self.clip_norm) + self.actor_optimizer = MpiAdam(var_list=self.actor.trainable_vars, + beta1=0.9, beta2=0.999, epsilon=1e-08) + + def setup_critic_optimizer(self): + logger.info('setting up critic optimizer') + normalized_critic_target_tf = tf.clip_by_value(normalize(self.critic_target, self.ret_rms), self.return_range[0], self.return_range[1]) + self.critic_loss = tf.reduce_mean(input_tensor=tf.square(self.normalized_critic_tf - normalized_critic_target_tf)) + if self.critic_l2_reg > 0.: + critic_reg_vars = [var for var in self.critic.trainable_vars if var.name.endswith('/w:0') and 'output' not in var.name] + for var in critic_reg_vars: + logger.info(' regularizing: {}'.format(var.name)) + logger.info(' applying l2 regularization with {}'.format(self.critic_l2_reg)) + critic_reg = tc.layers.apply_regularization( + tc.layers.l2_regularizer(self.critic_l2_reg), + weights_list=critic_reg_vars + ) + self.critic_loss += critic_reg + critic_shapes = [var.get_shape().as_list() for var in self.critic.trainable_vars] + critic_nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in critic_shapes]) + logger.info(' critic shapes: {}'.format(critic_shapes)) + logger.info(' critic params: {}'.format(critic_nb_params)) + self.critic_grads = U.flatgrad(self.critic_loss, self.critic.trainable_vars, clip_norm=self.clip_norm) + self.critic_optimizer = MpiAdam(var_list=self.critic.trainable_vars, + beta1=0.9, beta2=0.999, epsilon=1e-08) + + def setup_popart(self): + # See https://arxiv.org/pdf/1602.07714.pdf for details. + self.old_std = tf.compat.v1.placeholder(tf.float32, shape=[1], name='old_std') + new_std = self.ret_rms.std + self.old_mean = tf.compat.v1.placeholder(tf.float32, shape=[1], name='old_mean') + new_mean = self.ret_rms.mean + + self.renormalize_Q_outputs_op = [] + for vs in [self.critic.output_vars, self.target_critic.output_vars]: + assert len(vs) == 2 + M, b = vs + assert 'kernel' in M.name + assert 'bias' in b.name + assert M.get_shape()[-1] == 1 + assert b.get_shape()[-1] == 1 + self.renormalize_Q_outputs_op += [M.assign(M * self.old_std / new_std)] + self.renormalize_Q_outputs_op += [b.assign((b * self.old_std + self.old_mean - new_mean) / new_std)] + + def setup_stats(self): + ops = [] + names = [] + + if self.normalize_returns: + ops += [self.ret_rms.mean, self.ret_rms.std] + names += ['ret_rms_mean', 'ret_rms_std'] + + if self.normalize_observations: + ops += [tf.reduce_mean(input_tensor=self.obs_rms.mean), tf.reduce_mean(input_tensor=self.obs_rms.std)] + names += ['obs_rms_mean', 'obs_rms_std'] + + ops += [tf.reduce_mean(input_tensor=self.critic_tf)] + names += ['reference_Q_mean'] + ops += [reduce_std(self.critic_tf)] + names += ['reference_Q_std'] + + ops += [tf.reduce_mean(input_tensor=self.critic_with_actor_tf)] + names += ['reference_actor_Q_mean'] + ops += [reduce_std(self.critic_with_actor_tf)] + names += ['reference_actor_Q_std'] + + ops += [tf.reduce_mean(input_tensor=self.actor_tf)] + names += ['reference_action_mean'] + ops += [reduce_std(self.actor_tf)] + names += ['reference_action_std'] + + if self.param_noise: + ops += [tf.reduce_mean(input_tensor=self.perturbed_actor_tf)] + names += ['reference_perturbed_action_mean'] + ops += [reduce_std(self.perturbed_actor_tf)] + names += ['reference_perturbed_action_std'] + + self.stats_ops = ops + self.stats_names = names + + def step(self, obs, apply_noise=True, compute_Q=True): + if self.param_noise is not None and apply_noise: + actor_tf = self.perturbed_actor_tf + else: + actor_tf = self.actor_tf + feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])} + if compute_Q: + action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict) + else: + action = self.sess.run(actor_tf, feed_dict=feed_dict) + q = None + + if self.action_noise is not None and apply_noise: + noise = self.action_noise() + assert noise.shape == action[0].shape + action += noise + action = np.clip(action, self.action_range[0], self.action_range[1]) + + + return action, q, None, None + + def store_transition(self, obs0, action, reward, obs1, terminal1): + reward *= self.reward_scale + + B = obs0.shape[0] + for b in range(B): + self.memory.append(obs0[b], action[b], reward[b], obs1[b], terminal1[b]) + if self.normalize_observations: + self.obs_rms.update(np.array([obs0[b]])) + + def train(self): + # Get a batch. + batch = self.memory.sample(batch_size=self.batch_size) + + if self.normalize_returns and self.enable_popart: + old_mean, old_std, target_Q = self.sess.run([self.ret_rms.mean, self.ret_rms.std, self.target_Q], feed_dict={ + self.obs1: batch['obs1'], + self.rewards: batch['rewards'], + self.terminals1: batch['terminals1'].astype('float32'), + }) + self.ret_rms.update(target_Q.flatten()) + self.sess.run(self.renormalize_Q_outputs_op, feed_dict={ + self.old_std : np.array([old_std]), + self.old_mean : np.array([old_mean]), + }) + + # Run sanity check. Disabled by default since it slows down things considerably. + # print('running sanity check') + # target_Q_new, new_mean, new_std = self.sess.run([self.target_Q, self.ret_rms.mean, self.ret_rms.std], feed_dict={ + # self.obs1: batch['obs1'], + # self.rewards: batch['rewards'], + # self.terminals1: batch['terminals1'].astype('float32'), + # }) + # print(target_Q_new, target_Q, new_mean, new_std) + # assert (np.abs(target_Q - target_Q_new) < 1e-3).all() + else: + target_Q = self.sess.run(self.target_Q, feed_dict={ + self.obs1: batch['obs1'], + self.rewards: batch['rewards'], + self.terminals1: batch['terminals1'].astype('float32'), + }) + + # Get all gradients and perform a synced update. + ops = [self.actor_grads, self.actor_loss, self.critic_grads, self.critic_loss] + actor_grads, actor_loss, critic_grads, critic_loss = self.sess.run(ops, feed_dict={ + self.obs0: batch['obs0'], + self.actions: batch['actions'], + self.critic_target: target_Q, + }) + self.actor_optimizer.update(actor_grads, stepsize=self.actor_lr) + self.critic_optimizer.update(critic_grads, stepsize=self.critic_lr) + + return critic_loss, actor_loss + + def initialize(self, sess): + self.sess = sess + self.sess.run(tf.compat.v1.global_variables_initializer()) + self.actor_optimizer.sync() + self.critic_optimizer.sync() + self.sess.run(self.target_init_updates) + + def update_target_net(self): + self.sess.run(self.target_soft_updates) + + def get_stats(self): + if self.stats_sample is None: + # Get a sample and keep that fixed for all further computations. + # This allows us to estimate the change in value for the same set of inputs. + self.stats_sample = self.memory.sample(batch_size=self.batch_size) + values = self.sess.run(self.stats_ops, feed_dict={ + self.obs0: self.stats_sample['obs0'], + self.actions: self.stats_sample['actions'], + }) + + names = self.stats_names[:] + assert len(names) == len(values) + stats = dict(zip(names, values)) + + if self.param_noise is not None: + stats = {**stats, **self.param_noise.get_stats()} + + return stats + + def adapt_param_noise(self): + try: + from mpi4py import MPI + except ImportError: + MPI = None + + if self.param_noise is None: + return 0. + + # Perturb a separate copy of the policy to adjust the scale for the next "real" perturbation. + batch = self.memory.sample(batch_size=self.batch_size) + self.sess.run(self.perturb_adaptive_policy_ops, feed_dict={ + self.param_noise_stddev: self.param_noise.current_stddev, + }) + distance = self.sess.run(self.adaptive_policy_distance, feed_dict={ + self.obs0: batch['obs0'], + self.param_noise_stddev: self.param_noise.current_stddev, + }) + + if MPI is not None: + mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size() + else: + mean_distance = distance + + self.param_noise.adapt(mean_distance) + return mean_distance + + def reset(self): + # Reset internal state after an episode is complete. + if self.action_noise is not None: + self.action_noise.reset() + if self.param_noise is not None: + self.sess.run(self.perturb_policy_ops, feed_dict={ + self.param_noise_stddev: self.param_noise.current_stddev, + }) diff --git a/baselines/ddpg/memory.py b/baselines/ddpg/memory.py new file mode 100644 index 0000000000000000000000000000000000000000..715f4c08f0faccf7c4f7287c554a0ca715cca286 --- /dev/null +++ b/baselines/ddpg/memory.py @@ -0,0 +1,83 @@ +import numpy as np + + +class RingBuffer(object): + def __init__(self, maxlen, shape, dtype='float32'): + self.maxlen = maxlen + self.start = 0 + self.length = 0 + self.data = np.zeros((maxlen,) + shape).astype(dtype) + + def __len__(self): + return self.length + + def __getitem__(self, idx): + if idx < 0 or idx >= self.length: + raise KeyError() + return self.data[(self.start + idx) % self.maxlen] + + def get_batch(self, idxs): + return self.data[(self.start + idxs) % self.maxlen] + + def append(self, v): + if self.length < self.maxlen: + # We have space, simply increase the length. + self.length += 1 + elif self.length == self.maxlen: + # No space, "remove" the first item. + self.start = (self.start + 1) % self.maxlen + else: + # This should never happen. + raise RuntimeError() + self.data[(self.start + self.length - 1) % self.maxlen] = v + + +def array_min2d(x): + x = np.array(x) + if x.ndim >= 2: + return x + return x.reshape(-1, 1) + + +class Memory(object): + def __init__(self, limit, action_shape, observation_shape): + self.limit = limit + + self.observations0 = RingBuffer(limit, shape=observation_shape) + self.actions = RingBuffer(limit, shape=action_shape) + self.rewards = RingBuffer(limit, shape=(1,)) + self.terminals1 = RingBuffer(limit, shape=(1,)) + self.observations1 = RingBuffer(limit, shape=observation_shape) + + def sample(self, batch_size): + # Draw such that we always have a proceeding element. + batch_idxs = np.random.randint(self.nb_entries - 2, size=batch_size) + + obs0_batch = self.observations0.get_batch(batch_idxs) + obs1_batch = self.observations1.get_batch(batch_idxs) + action_batch = self.actions.get_batch(batch_idxs) + reward_batch = self.rewards.get_batch(batch_idxs) + terminal1_batch = self.terminals1.get_batch(batch_idxs) + + result = { + 'obs0': array_min2d(obs0_batch), + 'obs1': array_min2d(obs1_batch), + 'rewards': array_min2d(reward_batch), + 'actions': array_min2d(action_batch), + 'terminals1': array_min2d(terminal1_batch), + } + return result + + def append(self, obs0, action, reward, obs1, terminal1, training=True): + if not training: + return + + self.observations0.append(obs0) + self.actions.append(action) + self.rewards.append(reward) + self.observations1.append(obs1) + self.terminals1.append(terminal1) + + @property + def nb_entries(self): + return len(self.observations0) diff --git a/baselines/ddpg/models.py b/baselines/ddpg/models.py new file mode 100644 index 0000000000000000000000000000000000000000..24289bcadcd99585fc80341cf8041cededeb15f7 --- /dev/null +++ b/baselines/ddpg/models.py @@ -0,0 +1,51 @@ +import tensorflow as tf +from baselines.common.models import get_network_builder + + +class Model(object): + def __init__(self, name, network='mlp', **network_kwargs): + self.name = name + self.network_builder = get_network_builder(network)(**network_kwargs) + + @property + def vars(self): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope=self.name) + + @property + def trainable_vars(self): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, scope=self.name) + + @property + def perturbable_vars(self): + return [var for var in self.trainable_vars if 'LayerNorm' not in var.name] + + +class Actor(Model): + def __init__(self, nb_actions, name='actor', network='mlp', **network_kwargs): + super().__init__(name=name, network=network, **network_kwargs) + self.nb_actions = nb_actions + + def __call__(self, obs, reuse=False): + with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE): + x = self.network_builder(obs) + x = tf.compat.v1.layers.dense(x, self.nb_actions, kernel_initializer=tf.compat.v1.random_uniform_initializer(minval=-3e-3, maxval=3e-3)) + x = tf.nn.tanh(x) + return x + + +class Critic(Model): + def __init__(self, name='critic', network='mlp', **network_kwargs): + super().__init__(name=name, network=network, **network_kwargs) + self.layer_norm = True + + def __call__(self, obs, action, reuse=False): + with tf.compat.v1.variable_scope(self.name, reuse=tf.compat.v1.AUTO_REUSE): + x = tf.concat([obs, action], axis=-1) # this assumes observation and action can be concatenated + x = self.network_builder(x) + x = tf.compat.v1.layers.dense(x, 1, kernel_initializer=tf.compat.v1.random_uniform_initializer(minval=-3e-3, maxval=3e-3), name='output') + return x + + @property + def output_vars(self): + output_vars = [var for var in self.trainable_vars if 'output' in var.name] + return output_vars diff --git a/baselines/ddpg/noise.py b/baselines/ddpg/noise.py new file mode 100644 index 0000000000000000000000000000000000000000..c48d0d6a2288677e05950623f5c93d1e87c2071a --- /dev/null +++ b/baselines/ddpg/noise.py @@ -0,0 +1,67 @@ +import numpy as np + + +class AdaptiveParamNoiseSpec(object): + def __init__(self, initial_stddev=0.1, desired_action_stddev=0.1, adoption_coefficient=1.01): + self.initial_stddev = initial_stddev + self.desired_action_stddev = desired_action_stddev + self.adoption_coefficient = adoption_coefficient + + self.current_stddev = initial_stddev + + def adapt(self, distance): + if distance > self.desired_action_stddev: + # Decrease stddev. + self.current_stddev /= self.adoption_coefficient + else: + # Increase stddev. + self.current_stddev *= self.adoption_coefficient + + def get_stats(self): + stats = { + 'param_noise_stddev': self.current_stddev, + } + return stats + + def __repr__(self): + fmt = 'AdaptiveParamNoiseSpec(initial_stddev={}, desired_action_stddev={}, adoption_coefficient={})' + return fmt.format(self.initial_stddev, self.desired_action_stddev, self.adoption_coefficient) + + +class ActionNoise(object): + def reset(self): + pass + + +class NormalActionNoise(ActionNoise): + def __init__(self, mu, sigma): + self.mu = mu + self.sigma = sigma + + def __call__(self): + return np.random.normal(self.mu, self.sigma) + + def __repr__(self): + return 'NormalActionNoise(mu={}, sigma={})'.format(self.mu, self.sigma) + + +# Based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab +class OrnsteinUhlenbeckActionNoise(ActionNoise): + def __init__(self, mu, sigma, theta=.15, dt=1e-2, x0=None): + self.theta = theta + self.mu = mu + self.sigma = sigma + self.dt = dt + self.x0 = x0 + self.reset() + + def __call__(self): + x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape) + self.x_prev = x + return x + + def reset(self): + self.x_prev = self.x0 if self.x0 is not None else np.zeros_like(self.mu) + + def __repr__(self): + return 'OrnsteinUhlenbeckActionNoise(mu={}, sigma={})'.format(self.mu, self.sigma) diff --git a/baselines/ddpg/test_smoke.py b/baselines/ddpg/test_smoke.py new file mode 100644 index 0000000000000000000000000000000000000000..bd7eba66417c313606d8d05ee11f1f42c64dbf3f --- /dev/null +++ b/baselines/ddpg/test_smoke.py @@ -0,0 +1,16 @@ +from baselines.common.tests.util import smoketest +def _run(argstr): + smoketest('--alg=ddpg --env=Pendulum-v0 --num_timesteps=0 ' + argstr) + +def test_popart(): + _run('--normalize_returns=True --popart=True') + +def test_noise_normal(): + _run('--noise_type=normal_0.1') + +def test_noise_ou(): + _run('--noise_type=ou_0.1') + +def test_noise_adaptive(): + _run('--noise_type=adaptive-param_0.2,normal_0.1') + diff --git a/baselines/deepq/README.md b/baselines/deepq/README.md new file mode 100644 index 0000000000000000000000000000000000000000..51fcbefb723a7e51a4b518550cb2795d74f5fa45 --- /dev/null +++ b/baselines/deepq/README.md @@ -0,0 +1,37 @@ +## If you are curious. + +##### Train a Cartpole agent and watch it play once it converges! + +Here's a list of commands to run to quickly get a working example: + + + + +```bash +# Train model and save the results to cartpole_model.pkl +python -m baselines.run --alg=deepq --env=CartPole-v0 --save_path=./cartpole_model.pkl --num_timesteps=1e5 +# Load the model saved in cartpole_model.pkl and visualize the learned policy +python -m baselines.run --alg=deepq --env=CartPole-v0 --load_path=./cartpole_model.pkl --num_timesteps=0 --play +``` + +## If you wish to apply DQN to solve a problem. + +Check out our simple agent trained with one stop shop `deepq.learn` function. + +- [baselines/deepq/experiments/train_cartpole.py](experiments/train_cartpole.py) - train a Cartpole agent. + +In particular notice that once `deepq.learn` finishes training it returns `act` function which can be used to select actions in the environment. Once trained you can easily save it and load at later time. Complimentary file `enjoy_cartpole.py` loads and visualizes the learned policy. + +## If you wish to experiment with the algorithm + +##### Check out the examples + +- [baselines/deepq/experiments/custom_cartpole.py](experiments/custom_cartpole.py) - Cartpole training with more fine grained control over the internals of DQN algorithm. +- [baselines/deepq/defaults.py](defaults.py) - settings for training on atari. Run + +```bash +python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4 +``` +to train on Atari Pong (see more in repo-wide [README.md](../../README.md#training-models)) + + diff --git a/baselines/deepq/__init__.py b/baselines/deepq/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7f1a15cd42195321efdf29ce564aab830c247070 --- /dev/null +++ b/baselines/deepq/__init__.py @@ -0,0 +1,8 @@ +from baselines.deepq import models # noqa +from baselines.deepq.build_graph import build_act, build_train # noqa +from baselines.deepq.deepq import learn, load_act # noqa +from baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer # noqa + +def wrap_atari_dqn(env): + from baselines.common.atari_wrappers import wrap_deepmind + return wrap_deepmind(env, frame_stack=True, scale=False) diff --git a/baselines/deepq/build_graph.py b/baselines/deepq/build_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..d7da6b07660a1a1a15f0b968c3a2185c06864c5d --- /dev/null +++ b/baselines/deepq/build_graph.py @@ -0,0 +1,449 @@ +"""Deep Q learning graph + +The functions in this file can are used to create the following functions: + +======= act ======== + + Function to chose an action given an observation + + Parameters + ---------- + observation: object + Observation that can be feed into the output of make_obs_ph + stochastic: bool + if set to False all the actions are always deterministic (default False) + update_eps_ph: float + update epsilon a new value, if negative no update happens + (default: no update) + + Returns + ------- + Tensor of dtype tf.int64 and shape (BATCH_SIZE,) with an action to be performed for + every element of the batch. + + +======= act (in case of parameter noise) ======== + + Function to chose an action given an observation + + Parameters + ---------- + observation: object + Observation that can be feed into the output of make_obs_ph + stochastic: bool + if set to False all the actions are always deterministic (default False) + update_eps_ph: float + update epsilon to a new value, if negative no update happens + (default: no update) + reset_ph: bool + reset the perturbed policy by sampling a new perturbation + update_param_noise_threshold_ph: float + the desired threshold for the difference between non-perturbed and perturbed policy + update_param_noise_scale_ph: bool + whether or not to update the scale of the noise for the next time it is re-perturbed + + Returns + ------- + Tensor of dtype tf.int64 and shape (BATCH_SIZE,) with an action to be performed for + every element of the batch. + + +======= train ======= + + Function that takes a transition (s,a,r,s') and optimizes Bellman equation's error: + + td_error = Q(s,a) - (r + gamma * max_a' Q(s', a')) + loss = huber_loss[td_error] + + Parameters + ---------- + obs_t: object + a batch of observations + action: np.array + actions that were selected upon seeing obs_t. + dtype must be int32 and shape must be (batch_size,) + reward: np.array + immediate reward attained after executing those actions + dtype must be float32 and shape must be (batch_size,) + obs_tp1: object + observations that followed obs_t + done: np.array + 1 if obs_t was the last observation in the episode and 0 otherwise + obs_tp1 gets ignored, but must be of the valid shape. + dtype must be float32 and shape must be (batch_size,) + weight: np.array + imporance weights for every element of the batch (gradient is multiplied + by the importance weight) dtype must be float32 and shape must be (batch_size,) + + Returns + ------- + td_error: np.array + a list of differences between Q(s,a) and the target in Bellman's equation. + dtype is float32 and shape is (batch_size,) + +======= update_target ======== + + copy the parameters from optimized Q function to the target Q function. + In Q learning we actually optimize the following error: + + Q(s,a) - (r + gamma * max_a' Q'(s', a')) + + Where Q' is lagging behind Q to stablize the learning. For example for Atari + + Q' is set to Q once every 10000 updates training steps. + +""" +import tensorflow as tf +import baselines.common.tf_util as U + + +def scope_vars(scope, trainable_only=False): + """ + Get variables inside a scope + The scope can be specified as a string + Parameters + ---------- + scope: str or VariableScope + scope in which the variables reside. + trainable_only: bool + whether or not to return only the variables that were marked as trainable. + Returns + ------- + vars: [tf.Variable] + list of variables in `scope`. + """ + return tf.compat.v1.get_collection( + tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES if trainable_only else tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, + scope=scope if isinstance(scope, str) else scope.name + ) + + +def scope_name(): + """Returns the name of current scope as a string, e.g. deepq/q_func""" + return tf.compat.v1.get_variable_scope().name + + +def absolute_scope_name(relative_scope_name): + """Appends parent scope name to `relative_scope_name`""" + return scope_name() + "/" + relative_scope_name + + +def default_param_noise_filter(var): + if var not in tf.compat.v1.trainable_variables(): + # We never perturb non-trainable vars. + return False + if "fully_connected" in var.name: + # We perturb fully-connected layers. + return True + + # The remaining layers are likely conv or layer norm layers, which we do not wish to + # perturb (in the former case because they only extract features, in the latter case because + # we use them for normalization purposes). If you change your network, you will likely want + # to re-consider which layers to perturb and which to keep untouched. + return False + + +def build_act(make_obs_ph, q_func, num_actions, scope="deepq", reuse=None): + """Creates the act function: + + Parameters + ---------- + make_obs_ph: str -> tf.placeholder or TfInput + a function that take a name and creates a placeholder of input with that name + q_func: (tf.Variable, int, str, bool) -> tf.Variable + the model that takes the following inputs: + observation_in: object + the output of observation placeholder + num_actions: int + number of actions + scope: str + reuse: bool + should be passed to outer variable scope + and returns a tensor of shape (batch_size, num_actions) with values of every action. + num_actions: int + number of actions. + scope: str or VariableScope + optional scope for variable_scope. + reuse: bool or None + whether or not the variables should be reused. To be able to reuse the scope must be given. + + Returns + ------- + act: (tf.Variable, bool, float) -> tf.Variable + function to select and action given observation. +` See the top of the file for details. + """ + with tf.compat.v1.variable_scope(scope, reuse=reuse): + observations_ph = make_obs_ph("observation") + stochastic_ph = tf.compat.v1.placeholder(tf.bool, (), name="stochastic") + update_eps_ph = tf.compat.v1.placeholder(tf.float32, (), name="update_eps") + + eps = tf.compat.v1.get_variable("eps", (), initializer=tf.compat.v1.constant_initializer(0)) + + q_values = q_func(observations_ph.get(), num_actions, scope="q_func") + deterministic_actions = tf.argmax(input=q_values, axis=1) + + batch_size = tf.shape(input=observations_ph.get())[0] + random_actions = tf.random.uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64) + chose_random = tf.random.uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps + stochastic_actions = tf.compat.v1.where(chose_random, random_actions, deterministic_actions) + + output_actions = tf.cond(pred=stochastic_ph, true_fn=lambda: stochastic_actions, false_fn=lambda: deterministic_actions) + update_eps_expr = eps.assign(tf.cond(pred=update_eps_ph >= 0, true_fn=lambda: update_eps_ph, false_fn=lambda: eps)) + _act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph], + outputs=output_actions, + givens={update_eps_ph: -1.0, stochastic_ph: True}, + updates=[update_eps_expr]) + def act(ob, stochastic=True, update_eps=-1): + return _act(ob, stochastic, update_eps) + return act + + +def build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope="deepq", reuse=None, param_noise_filter_func=None): + """Creates the act function with support for parameter space noise exploration (https://arxiv.org/abs/1706.01905): + + Parameters + ---------- + make_obs_ph: str -> tf.placeholder or TfInput + a function that take a name and creates a placeholder of input with that name + q_func: (tf.Variable, int, str, bool) -> tf.Variable + the model that takes the following inputs: + observation_in: object + the output of observation placeholder + num_actions: int + number of actions + scope: str + reuse: bool + should be passed to outer variable scope + and returns a tensor of shape (batch_size, num_actions) with values of every action. + num_actions: int + number of actions. + scope: str or VariableScope + optional scope for variable_scope. + reuse: bool or None + whether or not the variables should be reused. To be able to reuse the scope must be given. + param_noise_filter_func: tf.Variable -> bool + function that decides whether or not a variable should be perturbed. Only applicable + if param_noise is True. If set to None, default_param_noise_filter is used by default. + + Returns + ------- + act: (tf.Variable, bool, float, bool, float, bool) -> tf.Variable + function to select and action given observation. +` See the top of the file for details. + """ + if param_noise_filter_func is None: + param_noise_filter_func = default_param_noise_filter + + with tf.compat.v1.variable_scope(scope, reuse=reuse): + observations_ph = make_obs_ph("observation") + stochastic_ph = tf.compat.v1.placeholder(tf.bool, (), name="stochastic") + update_eps_ph = tf.compat.v1.placeholder(tf.float32, (), name="update_eps") + update_param_noise_threshold_ph = tf.compat.v1.placeholder(tf.float32, (), name="update_param_noise_threshold") + update_param_noise_scale_ph = tf.compat.v1.placeholder(tf.bool, (), name="update_param_noise_scale") + reset_ph = tf.compat.v1.placeholder(tf.bool, (), name="reset") + + eps = tf.compat.v1.get_variable("eps", (), initializer=tf.compat.v1.constant_initializer(0)) + param_noise_scale = tf.compat.v1.get_variable("param_noise_scale", (), initializer=tf.compat.v1.constant_initializer(0.01), trainable=False) + param_noise_threshold = tf.compat.v1.get_variable("param_noise_threshold", (), initializer=tf.compat.v1.constant_initializer(0.05), trainable=False) + + # Unmodified Q. + q_values = q_func(observations_ph.get(), num_actions, scope="q_func") + + # Perturbable Q used for the actual rollout. + q_values_perturbed = q_func(observations_ph.get(), num_actions, scope="perturbed_q_func") + # We have to wrap this code into a function due to the way tf.cond() works. See + # https://stackoverflow.com/questions/37063952/confused-by-the-behavior-of-tf-cond for + # a more detailed discussion. + def perturb_vars(original_scope, perturbed_scope): + all_vars = scope_vars(absolute_scope_name(original_scope)) + all_perturbed_vars = scope_vars(absolute_scope_name(perturbed_scope)) + assert len(all_vars) == len(all_perturbed_vars) + perturb_ops = [] + for var, perturbed_var in zip(all_vars, all_perturbed_vars): + if param_noise_filter_func(perturbed_var): + # Perturb this variable. + op = tf.compat.v1.assign(perturbed_var, var + tf.random.normal(shape=tf.shape(input=var), mean=0., stddev=param_noise_scale)) + else: + # Do not perturb, just assign. + op = tf.compat.v1.assign(perturbed_var, var) + perturb_ops.append(op) + assert len(perturb_ops) == len(all_vars) + return tf.group(*perturb_ops) + + # Set up functionality to re-compute `param_noise_scale`. This perturbs yet another copy + # of the network and measures the effect of that perturbation in action space. If the perturbation + # is too big, reduce scale of perturbation, otherwise increase. + q_values_adaptive = q_func(observations_ph.get(), num_actions, scope="adaptive_q_func") + perturb_for_adaption = perturb_vars(original_scope="q_func", perturbed_scope="adaptive_q_func") + kl = tf.reduce_sum(input_tensor=tf.nn.softmax(q_values) * (tf.math.log(tf.nn.softmax(q_values)) - tf.math.log(tf.nn.softmax(q_values_adaptive))), axis=-1) + mean_kl = tf.reduce_mean(input_tensor=kl) + def update_scale(): + with tf.control_dependencies([perturb_for_adaption]): + update_scale_expr = tf.cond(pred=mean_kl < param_noise_threshold, + true_fn=lambda: param_noise_scale.assign(param_noise_scale * 1.01), + false_fn=lambda: param_noise_scale.assign(param_noise_scale / 1.01), + ) + return update_scale_expr + + # Functionality to update the threshold for parameter space noise. + update_param_noise_threshold_expr = param_noise_threshold.assign(tf.cond(pred=update_param_noise_threshold_ph >= 0, + true_fn=lambda: update_param_noise_threshold_ph, false_fn=lambda: param_noise_threshold)) + + # Put everything together. + deterministic_actions = tf.argmax(input=q_values_perturbed, axis=1) + batch_size = tf.shape(input=observations_ph.get())[0] + random_actions = tf.random.uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64) + chose_random = tf.random.uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps + stochastic_actions = tf.compat.v1.where(chose_random, random_actions, deterministic_actions) + + output_actions = tf.cond(pred=stochastic_ph, true_fn=lambda: stochastic_actions, false_fn=lambda: deterministic_actions) + update_eps_expr = eps.assign(tf.cond(pred=update_eps_ph >= 0, true_fn=lambda: update_eps_ph, false_fn=lambda: eps)) + updates = [ + update_eps_expr, + tf.cond(pred=reset_ph, true_fn=lambda: perturb_vars(original_scope="q_func", perturbed_scope="perturbed_q_func"), false_fn=lambda: tf.group(*[])), + tf.cond(pred=update_param_noise_scale_ph, true_fn=lambda: update_scale(), false_fn=lambda: tf.Variable(0., trainable=False)), + update_param_noise_threshold_expr, + ] + _act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph, reset_ph, update_param_noise_threshold_ph, update_param_noise_scale_ph], + outputs=output_actions, + givens={update_eps_ph: -1.0, stochastic_ph: True, reset_ph: False, update_param_noise_threshold_ph: False, update_param_noise_scale_ph: False}, + updates=updates) + def act(ob, reset=False, update_param_noise_threshold=False, update_param_noise_scale=False, stochastic=True, update_eps=-1): + return _act(ob, stochastic, update_eps, reset, update_param_noise_threshold, update_param_noise_scale) + return act + + +def build_train(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0, + double_q=True, scope="deepq", reuse=None, param_noise=False, param_noise_filter_func=None): + """Creates the train function: + + Parameters + ---------- + make_obs_ph: str -> tf.placeholder or TfInput + a function that takes a name and creates a placeholder of input with that name + q_func: (tf.Variable, int, str, bool) -> tf.Variable + the model that takes the following inputs: + observation_in: object + the output of observation placeholder + num_actions: int + number of actions + scope: str + reuse: bool + should be passed to outer variable scope + and returns a tensor of shape (batch_size, num_actions) with values of every action. + num_actions: int + number of actions + reuse: bool + whether or not to reuse the graph variables + optimizer: tf.train.Optimizer + optimizer to use for the Q-learning objective. + grad_norm_clipping: float or None + clip gradient norms to this value. If None no clipping is performed. + gamma: float + discount rate. + double_q: bool + if true will use Double Q Learning (https://arxiv.org/abs/1509.06461). + In general it is a good idea to keep it enabled. + scope: str or VariableScope + optional scope for variable_scope. + reuse: bool or None + whether or not the variables should be reused. To be able to reuse the scope must be given. + param_noise: bool + whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905) + param_noise_filter_func: tf.Variable -> bool + function that decides whether or not a variable should be perturbed. Only applicable + if param_noise is True. If set to None, default_param_noise_filter is used by default. + + Returns + ------- + act: (tf.Variable, bool, float) -> tf.Variable + function to select and action given observation. +` See the top of the file for details. + train: (object, np.array, np.array, object, np.array, np.array) -> np.array + optimize the error in Bellman's equation. +` See the top of the file for details. + update_target: () -> () + copy the parameters from optimized Q function to the target Q function. +` See the top of the file for details. + debug: {str: function} + a bunch of functions to print debug data like q_values. + """ + if param_noise: + act_f = build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse, + param_noise_filter_func=param_noise_filter_func) + else: + act_f = build_act(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse) + + with tf.compat.v1.variable_scope(scope, reuse=reuse): + # set up placeholders + obs_t_input = make_obs_ph("obs_t") + act_t_ph = tf.compat.v1.placeholder(tf.int32, [None], name="action") + rew_t_ph = tf.compat.v1.placeholder(tf.float32, [None], name="reward") + obs_tp1_input = make_obs_ph("obs_tp1") + done_mask_ph = tf.compat.v1.placeholder(tf.float32, [None], name="done") + importance_weights_ph = tf.compat.v1.placeholder(tf.float32, [None], name="weight") + + # q network evaluation + q_t = q_func(obs_t_input.get(), num_actions, scope="q_func", reuse=True) # reuse parameters from act + q_func_vars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope=tf.compat.v1.get_variable_scope().name + "/q_func") + + # target q network evalution + q_tp1 = q_func(obs_tp1_input.get(), num_actions, scope="target_q_func") + target_q_func_vars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope=tf.compat.v1.get_variable_scope().name + "/target_q_func") + + # q scores for actions which we know were selected in the given state. + q_t_selected = tf.reduce_sum(input_tensor=q_t * tf.one_hot(act_t_ph, num_actions), axis=1) + + # compute estimate of best possible value starting from state at t + 1 + if double_q: + q_tp1_using_online_net = q_func(obs_tp1_input.get(), num_actions, scope="q_func", reuse=True) + q_tp1_best_using_online_net = tf.argmax(input=q_tp1_using_online_net, axis=1) + q_tp1_best = tf.reduce_sum(input_tensor=q_tp1 * tf.one_hot(q_tp1_best_using_online_net, num_actions), axis=1) + else: + q_tp1_best = tf.reduce_max(input_tensor=q_tp1, axis=1) + q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best + + # compute RHS of bellman equation + q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked + + # compute the error (potentially clipped) + td_error = q_t_selected - tf.stop_gradient(q_t_selected_target) + errors = U.huber_loss(td_error) + weighted_error = tf.reduce_mean(input_tensor=importance_weights_ph * errors) + + # compute optimization op (potentially with gradient clipping) + if grad_norm_clipping is not None: + gradients = optimizer.compute_gradients(weighted_error, var_list=q_func_vars) + for i, (grad, var) in enumerate(gradients): + if grad is not None: + gradients[i] = (tf.clip_by_norm(grad, grad_norm_clipping), var) + optimize_expr = optimizer.apply_gradients(gradients) + else: + optimize_expr = optimizer.minimize(weighted_error, var_list=q_func_vars) + + # update_target_fn will be called periodically to copy Q network to target Q network + update_target_expr = [] + for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name), + sorted(target_q_func_vars, key=lambda v: v.name)): + update_target_expr.append(var_target.assign(var)) + update_target_expr = tf.group(*update_target_expr) + + # Create callable functions + train = U.function( + inputs=[ + obs_t_input, + act_t_ph, + rew_t_ph, + obs_tp1_input, + done_mask_ph, + importance_weights_ph + ], + outputs=td_error, + updates=[optimize_expr] + ) + update_target = U.function([], [], updates=[update_target_expr]) + + q_values = U.function([obs_t_input], q_t) + + return act_f, train, update_target, {'q_values': q_values} diff --git a/baselines/deepq/deepq.py b/baselines/deepq/deepq.py new file mode 100644 index 0000000000000000000000000000000000000000..d2d00b4ce89f9f1359867f39ac745bad7f0403c6 --- /dev/null +++ b/baselines/deepq/deepq.py @@ -0,0 +1,332 @@ +import os +import tempfile + +import tensorflow as tf +import zipfile +import cloudpickle +import numpy as np + +import baselines.common.tf_util as U +from baselines.common.tf_util import load_variables, save_variables +from baselines import logger +from baselines.common.schedules import LinearSchedule +from baselines.common import set_global_seeds + +from baselines import deepq +from baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer +from baselines.deepq.utils import ObservationInput + +from baselines.common.tf_util import get_session +from baselines.deepq.models import build_q_func + + +class ActWrapper(object): + def __init__(self, act, act_params): + self._act = act + self._act_params = act_params + self.initial_state = None + + @staticmethod + def load_act(path): + with open(path, "rb") as f: + model_data, act_params = cloudpickle.load(f) + act = deepq.build_act(**act_params) + sess = tf.compat.v1.Session() + sess.__enter__() + with tempfile.TemporaryDirectory() as td: + arc_path = os.path.join(td, "packed.zip") + with open(arc_path, "wb") as f: + f.write(model_data) + + zipfile.ZipFile(arc_path, 'r', zipfile.ZIP_DEFLATED).extractall(td) + load_variables(os.path.join(td, "model")) + + return ActWrapper(act, act_params) + + def __call__(self, *args, **kwargs): + return self._act(*args, **kwargs) + + def step(self, observation, **kwargs): + # DQN doesn't use RNNs so we ignore states and masks + kwargs.pop('S', None) + kwargs.pop('M', None) + return self._act([observation], **kwargs), None, None, None + + def save_act(self, path=None): + """Save model to a pickle located at `path`""" + if path is None: + path = os.path.join(logger.get_dir(), "model.pkl") + + with tempfile.TemporaryDirectory() as td: + save_variables(os.path.join(td, "model")) + arc_name = os.path.join(td, "packed.zip") + with zipfile.ZipFile(arc_name, 'w') as zipf: + for root, dirs, files in os.walk(td): + for fname in files: + file_path = os.path.join(root, fname) + if file_path != arc_name: + zipf.write(file_path, os.path.relpath(file_path, td)) + with open(arc_name, "rb") as f: + model_data = f.read() + with open(path, "wb") as f: + cloudpickle.dump((model_data, self._act_params), f) + + def save(self, path): + save_variables(path) + + +def load_act(path): + """Load act function that was returned by learn function. + + Parameters + ---------- + path: str + path to the act function pickle + + Returns + ------- + act: ActWrapper + function that takes a batch of observations + and returns actions. + """ + return ActWrapper.load_act(path) + + +def learn(env, + network, + seed=None, + lr=5e-4, + total_timesteps=100000, + buffer_size=50000, + exploration_fraction=0.1, + exploration_final_eps=0.02, + train_freq=1, + batch_size=32, + print_freq=100, + checkpoint_freq=10000, + checkpoint_path=None, + learning_starts=1000, + gamma=1.0, + target_network_update_freq=500, + prioritized_replay=False, + prioritized_replay_alpha=0.6, + prioritized_replay_beta0=0.4, + prioritized_replay_beta_iters=None, + prioritized_replay_eps=1e-6, + param_noise=False, + callback=None, + load_path=None, + **network_kwargs + ): + """Train a deepq model. + + Parameters + ------- + env: gym.Env + environment to train on + network: string or a function + neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models + (mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which + will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that) + seed: int or None + prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used. + lr: float + learning rate for adam optimizer + total_timesteps: int + number of env steps to optimizer for + buffer_size: int + size of the replay buffer + exploration_fraction: float + fraction of entire training period over which the exploration rate is annealed + exploration_final_eps: float + final value of random action probability + train_freq: int + update the model every `train_freq` steps. + batch_size: int + size of a batch sampled from replay buffer for training + print_freq: int + how often to print out training progress + set to None to disable printing + checkpoint_freq: int + how often to save the model. This is so that the best version is restored + at the end of the training. If you do not wish to restore the best version at + the end of the training set this variable to None. + learning_starts: int + how many steps of the model to collect transitions for before learning starts + gamma: float + discount factor + target_network_update_freq: int + update the target network every `target_network_update_freq` steps. + prioritized_replay: True + if True prioritized replay buffer will be used. + prioritized_replay_alpha: float + alpha parameter for prioritized replay buffer + prioritized_replay_beta0: float + initial value of beta for prioritized replay buffer + prioritized_replay_beta_iters: int + number of iterations over which beta will be annealed from initial value + to 1.0. If set to None equals to total_timesteps. + prioritized_replay_eps: float + epsilon to add to the TD errors when updating priorities. + param_noise: bool + whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905) + callback: (locals, globals) -> None + function called at every steps with state of the algorithm. + If callback returns true training stops. + load_path: str + path to load the model from. (default: None) + **network_kwargs + additional keyword arguments to pass to the network builder. + + Returns + ------- + act: ActWrapper + Wrapper over act function. Adds ability to save it and load it. + See header of baselines/deepq/categorical.py for details on the act function. + """ + # Create all the functions necessary to train the model + + sess = get_session() + set_global_seeds(seed) + + q_func = build_q_func(network, **network_kwargs) + + # capture the shape outside the closure so that the env object is not serialized + # by cloudpickle when serializing make_obs_ph + + observation_space = env.observation_space + def make_obs_ph(name): + return ObservationInput(observation_space, name=name) + + act, train, update_target, debug = deepq.build_train( + make_obs_ph=make_obs_ph, + q_func=q_func, + num_actions=env.action_space.n, + optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=lr), + gamma=gamma, + grad_norm_clipping=10, + param_noise=param_noise + ) + + act_params = { + 'make_obs_ph': make_obs_ph, + 'q_func': q_func, + 'num_actions': env.action_space.n, + } + + act = ActWrapper(act, act_params) + + # Create the replay buffer + if prioritized_replay: + replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha) + if prioritized_replay_beta_iters is None: + prioritized_replay_beta_iters = total_timesteps + beta_schedule = LinearSchedule(prioritized_replay_beta_iters, + initial_p=prioritized_replay_beta0, + final_p=1.0) + else: + replay_buffer = ReplayBuffer(buffer_size) + beta_schedule = None + # Create the schedule for exploration starting from 1. + exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps), + initial_p=1.0, + final_p=exploration_final_eps) + + # Initialize the parameters and copy them to the target network. + U.initialize() + update_target() + + episode_rewards = [0.0] + saved_mean_reward = None + obs = env.reset() + reset = True + + with tempfile.TemporaryDirectory() as td: + td = checkpoint_path or td + + model_file = os.path.join(td, "model") + model_saved = False + + if tf.train.latest_checkpoint(td) is not None: + load_variables(model_file) + logger.log('Loaded model from {}'.format(model_file)) + model_saved = True + elif load_path is not None: + load_variables(load_path) + logger.log('Loaded model from {}'.format(load_path)) + + + for t in range(total_timesteps): + if callback is not None: + if callback(locals(), globals()): + break + # Take action and update exploration to the newest value + kwargs = {} + if not param_noise: + update_eps = exploration.value(t) + update_param_noise_threshold = 0. + else: + update_eps = 0. + # Compute the threshold such that the KL divergence between perturbed and non-perturbed + # policy is comparable to eps-greedy exploration with eps = exploration.value(t). + # See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017 + # for detailed explanation. + update_param_noise_threshold = -np.log(1. - exploration.value(t) + exploration.value(t) / float(env.action_space.n)) + kwargs['reset'] = reset + kwargs['update_param_noise_threshold'] = update_param_noise_threshold + kwargs['update_param_noise_scale'] = True + action = act(np.array(obs)[None], update_eps=update_eps, **kwargs)[0] + env_action = action + reset = False + new_obs, rew, done, _ = env.step(env_action) + # Store transition in the replay buffer. + replay_buffer.add(obs, action, rew, new_obs, float(done)) + obs = new_obs + + episode_rewards[-1] += rew + if done: + obs = env.reset() + episode_rewards.append(0.0) + reset = True + + if t > learning_starts and t % train_freq == 0: + # Minimize the error in Bellman's equation on a batch sampled from replay buffer. + if prioritized_replay: + experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t)) + (obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience + else: + obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size) + weights, batch_idxes = np.ones_like(rewards), None + td_errors = train(obses_t, actions, rewards, obses_tp1, dones, weights) + if prioritized_replay: + new_priorities = np.abs(td_errors) + prioritized_replay_eps + replay_buffer.update_priorities(batch_idxes, new_priorities) + + if t > learning_starts and t % target_network_update_freq == 0: + # Update target network periodically. + update_target() + + mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1) + num_episodes = len(episode_rewards) + if done and print_freq is not None and len(episode_rewards) % print_freq == 0: + logger.record_tabular("steps", t) + logger.record_tabular("episodes", num_episodes) + logger.record_tabular("mean 100 episode reward", mean_100ep_reward) + logger.record_tabular("% time spent exploring", int(100 * exploration.value(t))) + logger.dump_tabular() + + if (checkpoint_freq is not None and t > learning_starts and + num_episodes > 100 and t % checkpoint_freq == 0): + if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward: + if print_freq is not None: + logger.log("Saving model due to mean reward increase: {} -> {}".format( + saved_mean_reward, mean_100ep_reward)) + save_variables(model_file) + model_saved = True + saved_mean_reward = mean_100ep_reward + if model_saved: + if print_freq is not None: + logger.log("Restored model with mean reward: {}".format(saved_mean_reward)) + load_variables(model_file) + + return act diff --git a/baselines/deepq/defaults.py b/baselines/deepq/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..d41fb180e8590ca5f72432c8f2ffac63ae99bfd9 --- /dev/null +++ b/baselines/deepq/defaults.py @@ -0,0 +1,21 @@ +def atari(): + return dict( + network='conv_only', + lr=1e-4, + buffer_size=10000, + exploration_fraction=0.1, + exploration_final_eps=0.01, + train_freq=4, + learning_starts=10000, + target_network_update_freq=1000, + gamma=0.99, + prioritized_replay=True, + prioritized_replay_alpha=0.6, + checkpoint_freq=10000, + checkpoint_path=None, + dueling=True + ) + +def retro(): + return atari() + diff --git a/baselines/deepq/experiments/__init__.py b/baselines/deepq/experiments/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/deepq/experiments/custom_cartpole.py b/baselines/deepq/experiments/custom_cartpole.py new file mode 100644 index 0000000000000000000000000000000000000000..937a7172bc67b1664721b1545a1021fc0b811837 --- /dev/null +++ b/baselines/deepq/experiments/custom_cartpole.py @@ -0,0 +1,79 @@ +import gym +import itertools +import numpy as np +import tensorflow as tf +import tensorflow.contrib.layers as layers + +import baselines.common.tf_util as U + +from baselines import logger +from baselines import deepq +from baselines.deepq.replay_buffer import ReplayBuffer +from baselines.deepq.utils import ObservationInput +from baselines.common.schedules import LinearSchedule + + +def model(inpt, num_actions, scope, reuse=False): + """This model takes as input an observation and returns values of all actions.""" + with tf.compat.v1.variable_scope(scope, reuse=reuse): + out = inpt + out = layers.fully_connected(out, num_outputs=64, activation_fn=tf.nn.tanh) + out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None) + return out + + +if __name__ == '__main__': + with U.make_session(num_cpu=8): + # Create the environment + env = gym.make("CartPole-v0") + # Create all the functions necessary to train the model + act, train, update_target, debug = deepq.build_train( + make_obs_ph=lambda name: ObservationInput(env.observation_space, name=name), + q_func=model, + num_actions=env.action_space.n, + optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=5e-4), + ) + # Create the replay buffer + replay_buffer = ReplayBuffer(50000) + # Create the schedule for exploration starting from 1 (every action is random) down to + # 0.02 (98% of actions are selected according to values predicted by the model). + exploration = LinearSchedule(schedule_timesteps=10000, initial_p=1.0, final_p=0.02) + + # Initialize the parameters and copy them to the target network. + U.initialize() + update_target() + + episode_rewards = [0.0] + obs = env.reset() + for t in itertools.count(): + # Take action and update exploration to the newest value + action = act(obs[None], update_eps=exploration.value(t))[0] + new_obs, rew, done, _ = env.step(action) + # Store transition in the replay buffer. + replay_buffer.add(obs, action, rew, new_obs, float(done)) + obs = new_obs + + episode_rewards[-1] += rew + if done: + obs = env.reset() + episode_rewards.append(0) + + is_solved = t > 100 and np.mean(episode_rewards[-101:-1]) >= 200 + if is_solved: + # Show off the result + env.render() + else: + # Minimize the error in Bellman's equation on a batch sampled from replay buffer. + if t > 1000: + obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(32) + train(obses_t, actions, rewards, obses_tp1, dones, np.ones_like(rewards)) + # Update target network periodically. + if t % 1000 == 0: + update_target() + + if done and len(episode_rewards) % 10 == 0: + logger.record_tabular("steps", t) + logger.record_tabular("episodes", len(episode_rewards)) + logger.record_tabular("mean episode reward", round(np.mean(episode_rewards[-101:-1]), 1)) + logger.record_tabular("% time spent exploring", int(100 * exploration.value(t))) + logger.dump_tabular() diff --git a/baselines/deepq/experiments/enjoy_cartpole.py b/baselines/deepq/experiments/enjoy_cartpole.py new file mode 100644 index 0000000000000000000000000000000000000000..b7d5ef15087fa6d9a47f178406908ecfd3257cab --- /dev/null +++ b/baselines/deepq/experiments/enjoy_cartpole.py @@ -0,0 +1,21 @@ +import gym + +from baselines import deepq + + +def main(): + env = gym.make("CartPole-v0") + act = deepq.learn(env, network='mlp', total_timesteps=0, load_path="cartpole_model.pkl") + + while True: + obs, done = env.reset(), False + episode_rew = 0 + while not done: + env.render() + obs, rew, done, _ = env.step(act(obs[None])[0]) + episode_rew += rew + print("Episode reward", episode_rew) + + +if __name__ == '__main__': + main() diff --git a/baselines/deepq/experiments/enjoy_mountaincar.py b/baselines/deepq/experiments/enjoy_mountaincar.py new file mode 100644 index 0000000000000000000000000000000000000000..2998bb66fbb8c31e262dd51ddf46c381837ee8c5 --- /dev/null +++ b/baselines/deepq/experiments/enjoy_mountaincar.py @@ -0,0 +1,27 @@ +import gym + +from baselines import deepq +from baselines.common import models + + +def main(): + env = gym.make("MountainCar-v0") + act = deepq.learn( + env, + network=models.mlp(num_layers=1, num_hidden=64), + total_timesteps=0, + load_path='mountaincar_model.pkl' + ) + + while True: + obs, done = env.reset(), False + episode_rew = 0 + while not done: + env.render() + obs, rew, done, _ = env.step(act(obs[None])[0]) + episode_rew += rew + print("Episode reward", episode_rew) + + +if __name__ == '__main__': + main() diff --git a/baselines/deepq/experiments/enjoy_pong.py b/baselines/deepq/experiments/enjoy_pong.py new file mode 100644 index 0000000000000000000000000000000000000000..0b118c75eb7b371c8f098756b972be19e9d91407 --- /dev/null +++ b/baselines/deepq/experiments/enjoy_pong.py @@ -0,0 +1,28 @@ +import gym +from baselines import deepq + + +def main(): + env = gym.make("PongNoFrameskip-v4") + env = deepq.wrap_atari_dqn(env) + model = deepq.learn( + env, + "conv_only", + convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], + hiddens=[256], + dueling=True, + total_timesteps=0 + ) + + while True: + obs, done = env.reset(), False + episode_rew = 0 + while not done: + env.render() + obs, rew, done, _ = env.step(model(obs[None])[0]) + episode_rew += rew + print("Episode reward", episode_rew) + + +if __name__ == '__main__': + main() diff --git a/baselines/deepq/experiments/train_cartpole.py b/baselines/deepq/experiments/train_cartpole.py new file mode 100644 index 0000000000000000000000000000000000000000..cfbbdc9c95446a91e71545bdbe501132480945f3 --- /dev/null +++ b/baselines/deepq/experiments/train_cartpole.py @@ -0,0 +1,30 @@ +import gym + +from baselines import deepq + + +def callback(lcl, _glb): + # stop training if reward exceeds 199 + is_solved = lcl['t'] > 100 and sum(lcl['episode_rewards'][-101:-1]) / 100 >= 199 + return is_solved + + +def main(): + env = gym.make("CartPole-v0") + act = deepq.learn( + env, + network='mlp', + lr=1e-3, + total_timesteps=100000, + buffer_size=50000, + exploration_fraction=0.1, + exploration_final_eps=0.02, + print_freq=10, + callback=callback + ) + print("Saving model to cartpole_model.pkl") + act.save("cartpole_model.pkl") + + +if __name__ == '__main__': + main() diff --git a/baselines/deepq/experiments/train_mountaincar.py b/baselines/deepq/experiments/train_mountaincar.py new file mode 100644 index 0000000000000000000000000000000000000000..fff678ad1e81bc4053160bf106ce9210f8769150 --- /dev/null +++ b/baselines/deepq/experiments/train_mountaincar.py @@ -0,0 +1,26 @@ +import gym + +from baselines import deepq +from baselines.common import models + + +def main(): + env = gym.make("MountainCar-v0") + # Enabling layer_norm here is import for parameter space noise! + act = deepq.learn( + env, + network=models.mlp(num_hidden=64, num_layers=1), + lr=1e-3, + total_timesteps=100000, + buffer_size=50000, + exploration_fraction=0.1, + exploration_final_eps=0.1, + print_freq=10, + param_noise=True + ) + print("Saving model to mountaincar_model.pkl") + act.save("mountaincar_model.pkl") + + +if __name__ == '__main__': + main() diff --git a/baselines/deepq/experiments/train_pong.py b/baselines/deepq/experiments/train_pong.py new file mode 100644 index 0000000000000000000000000000000000000000..8739aedfbd1eff586aec6ce506331fadf72ade0e --- /dev/null +++ b/baselines/deepq/experiments/train_pong.py @@ -0,0 +1,34 @@ +from baselines import deepq +from baselines import bench +from baselines import logger +from baselines.common.atari_wrappers import make_atari + + +def main(): + logger.configure() + env = make_atari('PongNoFrameskip-v4') + env = bench.Monitor(env, logger.get_dir()) + env = deepq.wrap_atari_dqn(env) + + model = deepq.learn( + env, + "conv_only", + convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], + hiddens=[256], + dueling=True, + lr=1e-4, + total_timesteps=int(1e7), + buffer_size=10000, + exploration_fraction=0.1, + exploration_final_eps=0.01, + train_freq=4, + learning_starts=10000, + target_network_update_freq=1000, + gamma=0.99, + ) + + model.save('pong_model.pkl') + env.close() + +if __name__ == '__main__': + main() diff --git a/baselines/deepq/models.py b/baselines/deepq/models.py new file mode 100644 index 0000000000000000000000000000000000000000..ee05cabbf32f4c34f4b77621aad3dcb34a5536f8 --- /dev/null +++ b/baselines/deepq/models.py @@ -0,0 +1,45 @@ +import tensorflow as tf +import tensorflow.contrib.layers as layers + + +def build_q_func(network, hiddens=[256], dueling=True, layer_norm=False, **network_kwargs): + if isinstance(network, str): + from baselines.common.models import get_network_builder + network = get_network_builder(network)(**network_kwargs) + + def q_func_builder(input_placeholder, num_actions, scope, reuse=False): + with tf.compat.v1.variable_scope(scope, reuse=reuse): + latent = network(input_placeholder) + if isinstance(latent, tuple): + if latent[1] is not None: + raise NotImplementedError("DQN is not compatible with recurrent policies yet") + latent = latent[0] + + latent = layers.flatten(latent) + + with tf.compat.v1.variable_scope("action_value"): + action_out = latent + for hidden in hiddens: + action_out = layers.fully_connected(action_out, num_outputs=hidden, activation_fn=None) + if layer_norm: + action_out = layers.layer_norm(action_out, center=True, scale=True) + action_out = tf.nn.relu(action_out) + action_scores = layers.fully_connected(action_out, num_outputs=num_actions, activation_fn=None) + + if dueling: + with tf.compat.v1.variable_scope("state_value"): + state_out = latent + for hidden in hiddens: + state_out = layers.fully_connected(state_out, num_outputs=hidden, activation_fn=None) + if layer_norm: + state_out = layers.layer_norm(state_out, center=True, scale=True) + state_out = tf.nn.relu(state_out) + state_score = layers.fully_connected(state_out, num_outputs=1, activation_fn=None) + action_scores_mean = tf.reduce_mean(input_tensor=action_scores, axis=1) + action_scores_centered = action_scores - tf.expand_dims(action_scores_mean, 1) + q_out = state_score + action_scores_centered + else: + q_out = action_scores + return q_out + + return q_func_builder diff --git a/baselines/deepq/replay_buffer.py b/baselines/deepq/replay_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..3ddf70860188c49ff2e09b87bdb6facac31ff92b --- /dev/null +++ b/baselines/deepq/replay_buffer.py @@ -0,0 +1,191 @@ +import numpy as np +import random + +from baselines.common.segment_tree import SumSegmentTree, MinSegmentTree + + +class ReplayBuffer(object): + def __init__(self, size): + """Create Replay buffer. + + Parameters + ---------- + size: int + Max number of transitions to store in the buffer. When the buffer + overflows the old memories are dropped. + """ + self._storage = [] + self._maxsize = size + self._next_idx = 0 + + def __len__(self): + return len(self._storage) + + def add(self, obs_t, action, reward, obs_tp1, done): + data = (obs_t, action, reward, obs_tp1, done) + + if self._next_idx >= len(self._storage): + self._storage.append(data) + else: + self._storage[self._next_idx] = data + self._next_idx = (self._next_idx + 1) % self._maxsize + + def _encode_sample(self, idxes): + obses_t, actions, rewards, obses_tp1, dones = [], [], [], [], [] + for i in idxes: + data = self._storage[i] + obs_t, action, reward, obs_tp1, done = data + obses_t.append(np.array(obs_t, copy=False)) + actions.append(np.array(action, copy=False)) + rewards.append(reward) + obses_tp1.append(np.array(obs_tp1, copy=False)) + dones.append(done) + return np.array(obses_t), np.array(actions), np.array(rewards), np.array(obses_tp1), np.array(dones) + + def sample(self, batch_size): + """Sample a batch of experiences. + + Parameters + ---------- + batch_size: int + How many transitions to sample. + + Returns + ------- + obs_batch: np.array + batch of observations + act_batch: np.array + batch of actions executed given obs_batch + rew_batch: np.array + rewards received as results of executing act_batch + next_obs_batch: np.array + next set of observations seen after executing act_batch + done_mask: np.array + done_mask[i] = 1 if executing act_batch[i] resulted in + the end of an episode and 0 otherwise. + """ + idxes = [random.randint(0, len(self._storage) - 1) for _ in range(batch_size)] + return self._encode_sample(idxes) + + +class PrioritizedReplayBuffer(ReplayBuffer): + def __init__(self, size, alpha): + """Create Prioritized Replay buffer. + + Parameters + ---------- + size: int + Max number of transitions to store in the buffer. When the buffer + overflows the old memories are dropped. + alpha: float + how much prioritization is used + (0 - no prioritization, 1 - full prioritization) + + See Also + -------- + ReplayBuffer.__init__ + """ + super(PrioritizedReplayBuffer, self).__init__(size) + assert alpha >= 0 + self._alpha = alpha + + it_capacity = 1 + while it_capacity < size: + it_capacity *= 2 + + self._it_sum = SumSegmentTree(it_capacity) + self._it_min = MinSegmentTree(it_capacity) + self._max_priority = 1.0 + + def add(self, *args, **kwargs): + """See ReplayBuffer.store_effect""" + idx = self._next_idx + super().add(*args, **kwargs) + self._it_sum[idx] = self._max_priority ** self._alpha + self._it_min[idx] = self._max_priority ** self._alpha + + def _sample_proportional(self, batch_size): + res = [] + p_total = self._it_sum.sum(0, len(self._storage) - 1) + every_range_len = p_total / batch_size + for i in range(batch_size): + mass = random.random() * every_range_len + i * every_range_len + idx = self._it_sum.find_prefixsum_idx(mass) + res.append(idx) + return res + + def sample(self, batch_size, beta): + """Sample a batch of experiences. + + compared to ReplayBuffer.sample + it also returns importance weights and idxes + of sampled experiences. + + + Parameters + ---------- + batch_size: int + How many transitions to sample. + beta: float + To what degree to use importance weights + (0 - no corrections, 1 - full correction) + + Returns + ------- + obs_batch: np.array + batch of observations + act_batch: np.array + batch of actions executed given obs_batch + rew_batch: np.array + rewards received as results of executing act_batch + next_obs_batch: np.array + next set of observations seen after executing act_batch + done_mask: np.array + done_mask[i] = 1 if executing act_batch[i] resulted in + the end of an episode and 0 otherwise. + weights: np.array + Array of shape (batch_size,) and dtype np.float32 + denoting importance weight of each sampled transition + idxes: np.array + Array of shape (batch_size,) and dtype np.int32 + idexes in buffer of sampled experiences + """ + assert beta > 0 + + idxes = self._sample_proportional(batch_size) + + weights = [] + p_min = self._it_min.min() / self._it_sum.sum() + max_weight = (p_min * len(self._storage)) ** (-beta) + + for idx in idxes: + p_sample = self._it_sum[idx] / self._it_sum.sum() + weight = (p_sample * len(self._storage)) ** (-beta) + weights.append(weight / max_weight) + weights = np.array(weights) + encoded_sample = self._encode_sample(idxes) + return tuple(list(encoded_sample) + [weights, idxes]) + + def update_priorities(self, idxes, priorities): + """Update priorities of sampled transitions. + + sets priority of transition at index idxes[i] in buffer + to priorities[i]. + + Parameters + ---------- + idxes: [int] + List of idxes of sampled transitions + priorities: [float] + List of updated priorities corresponding to + transitions at the sampled idxes denoted by + variable `idxes`. + """ + assert len(idxes) == len(priorities) + for idx, priority in zip(idxes, priorities): + assert priority > 0 + assert 0 <= idx < len(self._storage) + self._it_sum[idx] = priority ** self._alpha + self._it_min[idx] = priority ** self._alpha + + self._max_priority = max(self._max_priority, priority) diff --git a/baselines/deepq/utils.py b/baselines/deepq/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2065073fc577fd8031accd2154cab1e7086173cc --- /dev/null +++ b/baselines/deepq/utils.py @@ -0,0 +1,59 @@ +from baselines.common.input import observation_input +from baselines.common.tf_util import adjust_shape + +# ================================================================ +# Placeholders +# ================================================================ + + +class TfInput(object): + def __init__(self, name="(unnamed)"): + """Generalized Tensorflow placeholder. The main differences are: + - possibly uses multiple placeholders internally and returns multiple values + - can apply light postprocessing to the value feed to placeholder. + """ + self.name = name + + def get(self): + """Return the tf variable(s) representing the possibly postprocessed value + of placeholder(s). + """ + raise NotImplementedError + + def make_feed_dict(self, data): + """Given data input it to the placeholder(s).""" + raise NotImplementedError + + +class PlaceholderTfInput(TfInput): + def __init__(self, placeholder): + """Wrapper for regular tensorflow placeholder.""" + super().__init__(placeholder.name) + self._placeholder = placeholder + + def get(self): + return self._placeholder + + def make_feed_dict(self, data): + return {self._placeholder: adjust_shape(self._placeholder, data)} + + +class ObservationInput(PlaceholderTfInput): + def __init__(self, observation_space, name=None): + """Creates an input placeholder tailored to a specific observation space + + Parameters + ---------- + + observation_space: + observation space of the environment. Should be one of the gym.spaces types + name: str + tensorflow name of the underlying placeholder + """ + inpt, self.processed_inpt = observation_input(observation_space, name=name) + super().__init__(inpt) + + def get(self): + return self.processed_inpt + + diff --git a/baselines/gail/README.md b/baselines/gail/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1c1c1bb935c03b668f3340a9691c684f42dd165e --- /dev/null +++ b/baselines/gail/README.md @@ -0,0 +1,52 @@ +# Generative Adversarial Imitation Learning (GAIL) + +- Original paper: https://arxiv.org/abs/1606.03476 + +For results benchmarking on MuJoCo, please navigate to [here](result/gail-result.md) + +## If you want to train an imitation learning agent + +### Step 1: Download expert data + +Download the expert data into `./data`, [download link](https://drive.google.com/drive/folders/1h3H4AY_ZBx08hz-Ct0Nxxus-V1melu1U?usp=sharing) + +### Step 2: Run GAIL + +Run with single rank: + +```bash +python -m baselines.gail.run_mujoco +``` + +Run with multiple ranks: + +```bash +mpirun -np 16 python -m baselines.gail.run_mujoco +``` + +See help (`-h`) for more options. + +#### In case you want to run Behavior Cloning (BC) + +```bash +python -m baselines.gail.behavior_clone +``` + +See help (`-h`) for more options. + + +## Contributing + +Bug reports and pull requests are welcome on GitHub at https://github.com/openai/baselines/pulls. + +## Maintainers + +- Yuan-Hong Liao, andrewliao11_at_gmail_dot_com +- Ryan Julian, ryanjulian_at_gmail_dot_com + +## Others + +Thanks to the open source: + +- @openai/imitation +- @carpedm20/deep-rl-tensorflow diff --git a/baselines/gail/__init__.py b/baselines/gail/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/gail/adversary.py b/baselines/gail/adversary.py new file mode 100644 index 0000000000000000000000000000000000000000..931a8ab5b0da58397e880e16e03695f4186c4496 --- /dev/null +++ b/baselines/gail/adversary.py @@ -0,0 +1,87 @@ +''' +Reference: https://github.com/openai/imitation +I follow the architecture from the official repository +''' +import tensorflow as tf +import numpy as np + +from baselines.common.mpi_running_mean_std import RunningMeanStd +from baselines.common import tf_util as U + +def logsigmoid(a): + '''Equivalent to tf.log(tf.sigmoid(a))''' + return -tf.nn.softplus(-a) + +""" Reference: https://github.com/openai/imitation/blob/99fbccf3e060b6e6c739bdf209758620fcdefd3c/policyopt/thutil.py#L48-L51""" +def logit_bernoulli_entropy(logits): + ent = (1.-tf.nn.sigmoid(logits))*logits - logsigmoid(logits) + return ent + +class TransitionClassifier(object): + def __init__(self, env, hidden_size, entcoeff=0.001, lr_rate=1e-3, scope="adversary"): + self.scope = scope + self.observation_shape = env.observation_space.shape + self.actions_shape = env.action_space.shape + self.input_shape = tuple([o+a for o, a in zip(self.observation_shape, self.actions_shape)]) + self.num_actions = env.action_space.shape[0] + self.hidden_size = hidden_size + self.build_ph() + # Build grpah + generator_logits = self.build_graph(self.generator_obs_ph, self.generator_acs_ph, reuse=False) + expert_logits = self.build_graph(self.expert_obs_ph, self.expert_acs_ph, reuse=True) + # Build accuracy + generator_acc = tf.reduce_mean(input_tensor=tf.cast(tf.nn.sigmoid(generator_logits) < 0.5, dtype=tf.float32)) + expert_acc = tf.reduce_mean(input_tensor=tf.cast(tf.nn.sigmoid(expert_logits) > 0.5, dtype=tf.float32)) + # Build regression loss + # let x = logits, z = targets. + # z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x)) + generator_loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=generator_logits, labels=tf.zeros_like(generator_logits)) + generator_loss = tf.reduce_mean(input_tensor=generator_loss) + expert_loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=expert_logits, labels=tf.ones_like(expert_logits)) + expert_loss = tf.reduce_mean(input_tensor=expert_loss) + # Build entropy loss + logits = tf.concat([generator_logits, expert_logits], 0) + entropy = tf.reduce_mean(input_tensor=logit_bernoulli_entropy(logits)) + entropy_loss = -entcoeff*entropy + # Loss + Accuracy terms + self.losses = [generator_loss, expert_loss, entropy, entropy_loss, generator_acc, expert_acc] + self.loss_name = ["generator_loss", "expert_loss", "entropy", "entropy_loss", "generator_acc", "expert_acc"] + self.total_loss = generator_loss + expert_loss + entropy_loss + # Build Reward for policy + self.reward_op = -tf.math.log(1-tf.nn.sigmoid(generator_logits)+1e-8) + var_list = self.get_trainable_variables() + self.lossandgrad = U.function([self.generator_obs_ph, self.generator_acs_ph, self.expert_obs_ph, self.expert_acs_ph], + self.losses + [U.flatgrad(self.total_loss, var_list)]) + + def build_ph(self): + self.generator_obs_ph = tf.compat.v1.placeholder(tf.float32, (None, ) + self.observation_shape, name="observations_ph") + self.generator_acs_ph = tf.compat.v1.placeholder(tf.float32, (None, ) + self.actions_shape, name="actions_ph") + self.expert_obs_ph = tf.compat.v1.placeholder(tf.float32, (None, ) + self.observation_shape, name="expert_observations_ph") + self.expert_acs_ph = tf.compat.v1.placeholder(tf.float32, (None, ) + self.actions_shape, name="expert_actions_ph") + + def build_graph(self, obs_ph, acs_ph, reuse=False): + with tf.compat.v1.variable_scope(self.scope): + if reuse: + tf.compat.v1.get_variable_scope().reuse_variables() + + with tf.compat.v1.variable_scope("obfilter"): + self.obs_rms = RunningMeanStd(shape=self.observation_shape) + obs = (obs_ph - self.obs_rms.mean) / self.obs_rms.std + _input = tf.concat([obs, acs_ph], axis=1) # concatenate the two input -> form a transition + p_h1 = tf.contrib.layers.fully_connected(_input, self.hidden_size, activation_fn=tf.nn.tanh) + p_h2 = tf.contrib.layers.fully_connected(p_h1, self.hidden_size, activation_fn=tf.nn.tanh) + logits = tf.contrib.layers.fully_connected(p_h2, 1, activation_fn=tf.identity) + return logits + + def get_trainable_variables(self): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, self.scope) + + def get_reward(self, obs, acs): + sess = tf.compat.v1.get_default_session() + if len(obs.shape) == 1: + obs = np.expand_dims(obs, 0) + if len(acs.shape) == 1: + acs = np.expand_dims(acs, 0) + feed_dict = {self.generator_obs_ph: obs, self.generator_acs_ph: acs} + reward = sess.run(self.reward_op, feed_dict) + return reward diff --git a/baselines/gail/behavior_clone.py b/baselines/gail/behavior_clone.py new file mode 100644 index 0000000000000000000000000000000000000000..73994ff08dd1e27821624a3af5c674fd7687071b --- /dev/null +++ b/baselines/gail/behavior_clone.py @@ -0,0 +1,124 @@ +''' +The code is used to train BC imitator, or pretrained GAIL imitator +''' + +import argparse +import tempfile +import os.path as osp +import gym +import logging +from tqdm import tqdm + +import tensorflow as tf + +from baselines.gail import mlp_policy +from baselines import bench +from baselines import logger +from baselines.common import set_global_seeds, tf_util as U +from baselines.common.misc_util import boolean_flag +from baselines.common.mpi_adam import MpiAdam +from baselines.gail.run_mujoco import runner +from baselines.gail.dataset.mujoco_dset import Mujoco_Dset + + +def argsparser(): + parser = argparse.ArgumentParser("Tensorflow Implementation of Behavior Cloning") + parser.add_argument('--env_id', help='environment ID', default='Hopper-v2') + parser.add_argument('--seed', help='RNG seed', type=int, default=0) + parser.add_argument('--expert_path', type=str, default='data/deterministic.trpo.Hopper.0.00.npz') + parser.add_argument('--checkpoint_dir', help='the directory to save model', default='checkpoint') + parser.add_argument('--log_dir', help='the directory to save log file', default='log') + # Mujoco Dataset Configuration + parser.add_argument('--traj_limitation', type=int, default=-1) + # Network Configuration (Using MLP Policy) + parser.add_argument('--policy_hidden_size', type=int, default=100) + # for evaluatation + boolean_flag(parser, 'stochastic_policy', default=False, help='use stochastic/deterministic policy to evaluate') + boolean_flag(parser, 'save_sample', default=False, help='save the trajectories or not') + parser.add_argument('--BC_max_iter', help='Max iteration for training BC', type=int, default=1e5) + return parser.parse_args() + + +def learn(env, policy_func, dataset, optim_batch_size=128, max_iters=1e4, + adam_epsilon=1e-5, optim_stepsize=3e-4, + ckpt_dir=None, log_dir=None, task_name=None, + verbose=False): + + val_per_iter = int(max_iters/10) + ob_space = env.observation_space + ac_space = env.action_space + pi = policy_func("pi", ob_space, ac_space) # Construct network for new policy + # placeholder + ob = U.get_placeholder_cached(name="ob") + ac = pi.pdtype.sample_placeholder([None]) + stochastic = U.get_placeholder_cached(name="stochastic") + loss = tf.reduce_mean(input_tensor=tf.square(ac-pi.ac)) + var_list = pi.get_trainable_variables() + adam = MpiAdam(var_list, epsilon=adam_epsilon) + lossandgrad = U.function([ob, ac, stochastic], [loss]+[U.flatgrad(loss, var_list)]) + + U.initialize() + adam.sync() + logger.log("Pretraining with Behavior Cloning...") + for iter_so_far in tqdm(range(int(max_iters))): + ob_expert, ac_expert = dataset.get_next_batch(optim_batch_size, 'train') + train_loss, g = lossandgrad(ob_expert, ac_expert, True) + adam.update(g, optim_stepsize) + if verbose and iter_so_far % val_per_iter == 0: + ob_expert, ac_expert = dataset.get_next_batch(-1, 'val') + val_loss, _ = lossandgrad(ob_expert, ac_expert, True) + logger.log("Training loss: {}, Validation loss: {}".format(train_loss, val_loss)) + + if ckpt_dir is None: + savedir_fname = tempfile.TemporaryDirectory().name + else: + savedir_fname = osp.join(ckpt_dir, task_name) + U.save_variables(savedir_fname, variables=pi.get_variables()) + return savedir_fname + + +def get_task_name(args): + task_name = 'BC' + task_name += '.{}'.format(args.env_id.split("-")[0]) + task_name += '.traj_limitation_{}'.format(args.traj_limitation) + task_name += ".seed_{}".format(args.seed) + return task_name + + +def main(args): + U.make_session(num_cpu=1).__enter__() + set_global_seeds(args.seed) + env = gym.make(args.env_id) + + def policy_fn(name, ob_space, ac_space, reuse=False): + return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, + reuse=reuse, hid_size=args.policy_hidden_size, num_hid_layers=2) + env = bench.Monitor(env, logger.get_dir() and + osp.join(logger.get_dir(), "monitor.json")) + env.seed(args.seed) + gym.logger.setLevel(logging.WARN) + task_name = get_task_name(args) + args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name) + args.log_dir = osp.join(args.log_dir, task_name) + dataset = Mujoco_Dset(expert_path=args.expert_path, traj_limitation=args.traj_limitation) + savedir_fname = learn(env, + policy_fn, + dataset, + max_iters=args.BC_max_iter, + ckpt_dir=args.checkpoint_dir, + log_dir=args.log_dir, + task_name=task_name, + verbose=True) + avg_len, avg_ret = runner(env, + policy_fn, + savedir_fname, + timesteps_per_batch=1024, + number_trajs=10, + stochastic_policy=args.stochastic_policy, + save=args.save_sample, + reuse=True) + + +if __name__ == '__main__': + args = argsparser() + main(args) diff --git a/baselines/gail/dataset/__init__.py b/baselines/gail/dataset/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/gail/dataset/mujoco_dset.py b/baselines/gail/dataset/mujoco_dset.py new file mode 100644 index 0000000000000000000000000000000000000000..c478af8cfbb48594feb00ae4435965c02b057e28 --- /dev/null +++ b/baselines/gail/dataset/mujoco_dset.py @@ -0,0 +1,114 @@ +''' +Data structure of the input .npz: +the data is save in python dictionary format with keys: 'acs', 'ep_rets', 'rews', 'obs' +the values of each item is a list storing the expert trajectory sequentially +a transition can be: (data['obs'][t], data['acs'][t], data['obs'][t+1]) and get reward data['rews'][t] +''' + +from baselines import logger +import numpy as np + + +class Dset(object): + def __init__(self, inputs, labels, randomize): + self.inputs = inputs + self.labels = labels + assert len(self.inputs) == len(self.labels) + self.randomize = randomize + self.num_pairs = len(inputs) + self.init_pointer() + + def init_pointer(self): + self.pointer = 0 + if self.randomize: + idx = np.arange(self.num_pairs) + np.random.shuffle(idx) + self.inputs = self.inputs[idx, :] + self.labels = self.labels[idx, :] + + def get_next_batch(self, batch_size): + # if batch_size is negative -> return all + if batch_size < 0: + return self.inputs, self.labels + if self.pointer + batch_size >= self.num_pairs: + self.init_pointer() + end = self.pointer + batch_size + inputs = self.inputs[self.pointer:end, :] + labels = self.labels[self.pointer:end, :] + self.pointer = end + return inputs, labels + + +class Mujoco_Dset(object): + def __init__(self, expert_path, train_fraction=0.7, traj_limitation=-1, randomize=True): + traj_data = np.load(expert_path) + if traj_limitation < 0: + traj_limitation = len(traj_data['obs']) + obs = traj_data['obs'][:traj_limitation] + acs = traj_data['acs'][:traj_limitation] + + # obs, acs: shape (N, L, ) + S where N = # episodes, L = episode length + # and S is the environment observation/action space. + # Flatten to (N * L, prod(S)) + if len(obs.shape) > 2: + self.obs = np.reshape(obs, [-1, np.prod(obs.shape[2:])]) + self.acs = np.reshape(acs, [-1, np.prod(acs.shape[2:])]) + else: + self.obs = np.vstack(obs) + self.acs = np.vstack(acs) + + self.rets = traj_data['ep_rets'][:traj_limitation] + self.avg_ret = sum(self.rets)/len(self.rets) + self.std_ret = np.std(np.array(self.rets)) + if len(self.acs) > 2: + self.acs = np.squeeze(self.acs) + assert len(self.obs) == len(self.acs) + self.num_traj = min(traj_limitation, len(traj_data['obs'])) + self.num_transition = len(self.obs) + self.randomize = randomize + self.dset = Dset(self.obs, self.acs, self.randomize) + # for behavior cloning + self.train_set = Dset(self.obs[:int(self.num_transition*train_fraction), :], + self.acs[:int(self.num_transition*train_fraction), :], + self.randomize) + self.val_set = Dset(self.obs[int(self.num_transition*train_fraction):, :], + self.acs[int(self.num_transition*train_fraction):, :], + self.randomize) + self.log_info() + + def log_info(self): + logger.log("Total trajectories: %d" % self.num_traj) + logger.log("Total transitions: %d" % self.num_transition) + logger.log("Average returns: %f" % self.avg_ret) + logger.log("Std for returns: %f" % self.std_ret) + + def get_next_batch(self, batch_size, split=None): + if split is None: + return self.dset.get_next_batch(batch_size) + elif split == 'train': + return self.train_set.get_next_batch(batch_size) + elif split == 'val': + return self.val_set.get_next_batch(batch_size) + else: + raise NotImplementedError + + def plot(self): + import matplotlib.pyplot as plt + plt.hist(self.rets) + plt.savefig("histogram_rets.png") + plt.close() + + +def test(expert_path, traj_limitation, plot): + dset = Mujoco_Dset(expert_path, traj_limitation=traj_limitation) + if plot: + dset.plot() + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("--expert_path", type=str, default="../data/deterministic.trpo.Hopper.0.00.npz") + parser.add_argument("--traj_limitation", type=int, default=None) + parser.add_argument("--plot", type=bool, default=False) + args = parser.parse_args() + test(args.expert_path, args.traj_limitation, args.plot) diff --git a/baselines/gail/gail-eval.py b/baselines/gail/gail-eval.py new file mode 100644 index 0000000000000000000000000000000000000000..1148cb309ccb4712ac41743b4dbf89549e745f4f --- /dev/null +++ b/baselines/gail/gail-eval.py @@ -0,0 +1,147 @@ +''' +This code is used to evalaute the imitators trained with different number of trajectories +and plot the results in the same figure for easy comparison. +''' + +import argparse +import os +import glob +import gym + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +from baselines.gail import run_mujoco +from baselines.gail import mlp_policy +from baselines.common import set_global_seeds, tf_util as U +from baselines.common.misc_util import boolean_flag +from baselines.gail.dataset.mujoco_dset import Mujoco_Dset + + +plt.style.use('ggplot') +CONFIG = { + 'traj_limitation': [1, 5, 10, 50], +} + + +def load_dataset(expert_path): + dataset = Mujoco_Dset(expert_path=expert_path) + return dataset + + +def argsparser(): + parser = argparse.ArgumentParser('Do evaluation') + parser.add_argument('--seed', type=int, default=0) + parser.add_argument('--policy_hidden_size', type=int, default=100) + parser.add_argument('--env', type=str, choices=['Hopper', 'Walker2d', 'HalfCheetah', + 'Humanoid', 'HumanoidStandup']) + boolean_flag(parser, 'stochastic_policy', default=False, help='use stochastic/deterministic policy to evaluate') + return parser.parse_args() + + +def evaluate_env(env_name, seed, policy_hidden_size, stochastic, reuse, prefix): + + def get_checkpoint_dir(checkpoint_list, limit, prefix): + for checkpoint in checkpoint_list: + if ('limitation_'+str(limit) in checkpoint) and (prefix in checkpoint): + return checkpoint + return None + + def policy_fn(name, ob_space, ac_space, reuse=False): + return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, + reuse=reuse, hid_size=policy_hidden_size, num_hid_layers=2) + + data_path = os.path.join('data', 'deterministic.trpo.' + env_name + '.0.00.npz') + dataset = load_dataset(data_path) + checkpoint_list = glob.glob(os.path.join('checkpoint', '*' + env_name + ".*")) + log = { + 'traj_limitation': [], + 'upper_bound': [], + 'avg_ret': [], + 'avg_len': [], + 'normalized_ret': [] + } + for i, limit in enumerate(CONFIG['traj_limitation']): + # Do one evaluation + upper_bound = sum(dataset.rets[:limit])/limit + checkpoint_dir = get_checkpoint_dir(checkpoint_list, limit, prefix=prefix) + checkpoint_path = tf.train.latest_checkpoint(checkpoint_dir) + env = gym.make(env_name + '-v1') + env.seed(seed) + print('Trajectory limitation: {}, Load checkpoint: {}, '.format(limit, checkpoint_path)) + avg_len, avg_ret = run_mujoco.runner(env, + policy_fn, + checkpoint_path, + timesteps_per_batch=1024, + number_trajs=10, + stochastic_policy=stochastic, + reuse=((i != 0) or reuse)) + normalized_ret = avg_ret/upper_bound + print('Upper bound: {}, evaluation returns: {}, normalized scores: {}'.format( + upper_bound, avg_ret, normalized_ret)) + log['traj_limitation'].append(limit) + log['upper_bound'].append(upper_bound) + log['avg_ret'].append(avg_ret) + log['avg_len'].append(avg_len) + log['normalized_ret'].append(normalized_ret) + env.close() + return log + + +def plot(env_name, bc_log, gail_log, stochastic): + upper_bound = bc_log['upper_bound'] + bc_avg_ret = bc_log['avg_ret'] + gail_avg_ret = gail_log['avg_ret'] + plt.plot(CONFIG['traj_limitation'], upper_bound) + plt.plot(CONFIG['traj_limitation'], bc_avg_ret) + plt.plot(CONFIG['traj_limitation'], gail_avg_ret) + plt.xlabel('Number of expert trajectories') + plt.ylabel('Accumulated reward') + plt.title('{} unnormalized scores'.format(env_name)) + plt.legend(['expert', 'bc-imitator', 'gail-imitator'], loc='lower right') + plt.grid(b=True, which='major', color='gray', linestyle='--') + if stochastic: + title_name = 'result/{}-unnormalized-stochastic-scores.png'.format(env_name) + else: + title_name = 'result/{}-unnormalized-deterministic-scores.png'.format(env_name) + plt.savefig(title_name) + plt.close() + + bc_normalized_ret = bc_log['normalized_ret'] + gail_normalized_ret = gail_log['normalized_ret'] + plt.plot(CONFIG['traj_limitation'], np.ones(len(CONFIG['traj_limitation']))) + plt.plot(CONFIG['traj_limitation'], bc_normalized_ret) + plt.plot(CONFIG['traj_limitation'], gail_normalized_ret) + plt.xlabel('Number of expert trajectories') + plt.ylabel('Normalized performance') + plt.title('{} normalized scores'.format(env_name)) + plt.legend(['expert', 'bc-imitator', 'gail-imitator'], loc='lower right') + plt.grid(b=True, which='major', color='gray', linestyle='--') + if stochastic: + title_name = 'result/{}-normalized-stochastic-scores.png'.format(env_name) + else: + title_name = 'result/{}-normalized-deterministic-scores.png'.format(env_name) + plt.ylim(0, 1.6) + plt.savefig(title_name) + plt.close() + + +def main(args): + U.make_session(num_cpu=1).__enter__() + set_global_seeds(args.seed) + print('Evaluating {}'.format(args.env)) + bc_log = evaluate_env(args.env, args.seed, args.policy_hidden_size, + args.stochastic_policy, False, 'BC') + print('Evaluation for {}'.format(args.env)) + print(bc_log) + gail_log = evaluate_env(args.env, args.seed, args.policy_hidden_size, + args.stochastic_policy, True, 'gail') + print('Evaluation for {}'.format(args.env)) + print(gail_log) + plot(args.env, bc_log, gail_log, args.stochastic_policy) + + +if __name__ == '__main__': + args = argsparser() + main(args) diff --git a/baselines/gail/mlp_policy.py b/baselines/gail/mlp_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..430a06138ad7cb6277d544a93814ddf5cdb28cd2 --- /dev/null +++ b/baselines/gail/mlp_policy.py @@ -0,0 +1,75 @@ +''' +from baselines/ppo1/mlp_policy.py and add simple modification +(1) add reuse argument +(2) cache the `stochastic` placeholder +''' +import tensorflow as tf +import gym + +import baselines.common.tf_util as U +from baselines.common.mpi_running_mean_std import RunningMeanStd +from baselines.common.distributions import make_pdtype +from baselines.acktr.utils import dense + + +class MlpPolicy(object): + recurrent = False + + def __init__(self, name, reuse=False, *args, **kwargs): + with tf.compat.v1.variable_scope(name): + if reuse: + tf.compat.v1.get_variable_scope().reuse_variables() + self._init(*args, **kwargs) + self.scope = tf.compat.v1.get_variable_scope().name + + def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True): + assert isinstance(ob_space, gym.spaces.Box) + + self.pdtype = pdtype = make_pdtype(ac_space) + sequence_length = None + + ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape)) + + with tf.compat.v1.variable_scope("obfilter"): + self.ob_rms = RunningMeanStd(shape=ob_space.shape) + + obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0) + last_out = obz + for i in range(num_hid_layers): + last_out = tf.nn.tanh(dense(last_out, hid_size, "vffc%i" % (i+1), weight_init=U.normc_initializer(1.0))) + self.vpred = dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:, 0] + + last_out = obz + for i in range(num_hid_layers): + last_out = tf.nn.tanh(dense(last_out, hid_size, "polfc%i" % (i+1), weight_init=U.normc_initializer(1.0))) + + if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box): + mean = dense(last_out, pdtype.param_shape()[0]//2, "polfinal", U.normc_initializer(0.01)) + logstd = tf.compat.v1.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.compat.v1.zeros_initializer()) + pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1) + else: + pdparam = dense(last_out, pdtype.param_shape()[0], "polfinal", U.normc_initializer(0.01)) + + self.pd = pdtype.pdfromflat(pdparam) + + self.state_in = [] + self.state_out = [] + + # change for BC + stochastic = U.get_placeholder(name="stochastic", dtype=tf.bool, shape=()) + ac = U.switch(stochastic, self.pd.sample(), self.pd.mode()) + self.ac = ac + self._act = U.function([stochastic, ob], [ac, self.vpred]) + + def act(self, stochastic, ob): + ac1, vpred1 = self._act(stochastic, ob[None]) + return ac1[0], vpred1[0] + + def get_variables(self): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, self.scope) + + def get_trainable_variables(self): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, self.scope) + + def get_initial_state(self): + return [] diff --git a/baselines/gail/result/HalfCheetah-normalized-deterministic-scores.png b/baselines/gail/result/HalfCheetah-normalized-deterministic-scores.png new file mode 100644 index 0000000000000000000000000000000000000000..ce4282f01639b9cfbdde40566c5c8bb7510f8e2a Binary files /dev/null and b/baselines/gail/result/HalfCheetah-normalized-deterministic-scores.png differ diff --git a/baselines/gail/result/HalfCheetah-normalized-stochastic-scores.png 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b/baselines/gail/result/Walker2d-unnormalized-stochastic-scores.png differ diff --git a/baselines/gail/result/gail-result.md b/baselines/gail/result/gail-result.md new file mode 100644 index 0000000000000000000000000000000000000000..8ecc9ed1ebeef40cc3631c49a07ff2b224b1065c --- /dev/null +++ b/baselines/gail/result/gail-result.md @@ -0,0 +1,53 @@ +# Results of GAIL/BC on Mujoco + +Here's the extensive experimental results of applying GAIL/BC on Mujoco environments, including +Hopper-v1, Walker2d-v1, HalfCheetah-v1, Humanoid-v1, HumanoidStandup-v1. Every imitator is evaluated with seed to be 0. + +## Results + +### Training through iterations + +- Hoppers-v1 + + +- HalfCheetah-v1 + + +- Walker2d-v1 + + +- Humanoid-v1 + + +- HumanoidStandup-v1 + + +For details (e.g., adversarial loss, discriminator accuracy, etc.) about GAIL training, please see [here](https://drive.google.com/drive/folders/1nnU8dqAV9i37-_5_vWIspyFUJFQLCsDD?usp=sharing) + +### Determinstic Policy (Set std=0) +| | Un-normalized | Normalized | +|---|---|---| +| Hopper-v1 | | | +| HalfCheetah-v1 | | | +| Walker2d-v1 | | | +| Humanoid-v1 | | | +| HumanoidStandup-v1 | | | + +### Stochatic Policy +| | Un-normalized | Normalized | +|---|---|---| +| Hopper-v1 | | | +| HalfCheetah-v1 | | | +| Walker2d-v1 | | | +| Humanoid-v1 | | | +| HumanoidStandup-v1 | | | + +### details about GAIL imitator + +For all environments, the +imitator is trained with 1, 5, 10, 50 trajectories, where each trajectory contains at most +1024 transitions, and seed 0, 1, 2, 3, respectively. + +### details about the BC imitators + +All BC imitators are trained with seed 0. diff --git a/baselines/gail/result/halfcheetah-training.png b/baselines/gail/result/halfcheetah-training.png new file mode 100644 index 0000000000000000000000000000000000000000..36ebf7e0d8ef8ce18fe4f3eff9b9988b08afe354 --- /dev/null +++ b/baselines/gail/result/halfcheetah-training.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fac1ba8652022e651452df31586ae7a9e95fe78e2484d0c2a07865502b4adaf3 +size 515676 diff --git a/baselines/gail/result/hopper-training.png b/baselines/gail/result/hopper-training.png new file mode 100644 index 0000000000000000000000000000000000000000..9a1732f4a77d9b4605261b50bf356263eca7ef81 --- /dev/null +++ b/baselines/gail/result/hopper-training.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a6afe8c0224a57a4cd1bdb1b58a122832559e33c706a796cb43fd70cbadd7896 +size 547002 diff --git a/baselines/gail/result/humanoid-training.png b/baselines/gail/result/humanoid-training.png new file mode 100644 index 0000000000000000000000000000000000000000..f98ec62bbfb1f358914c04aa1de6b3258383cd68 --- /dev/null +++ b/baselines/gail/result/humanoid-training.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b516f89351cfb53fbed75df80dfe845ab1a2dbafb30e348caa17156d6d501731 +size 551074 diff --git a/baselines/gail/result/humanoidstandup-training.png b/baselines/gail/result/humanoidstandup-training.png new file mode 100644 index 0000000000000000000000000000000000000000..ce59b8812c8a0bf9b7dda31392b987ada4c3a248 --- /dev/null +++ b/baselines/gail/result/humanoidstandup-training.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:204464b59033e3515d2d2678e698dd5e03ee7b058d1ab8bbdbe89b5c52c95cdf +size 700385 diff --git a/baselines/gail/result/walker2d-training.png b/baselines/gail/result/walker2d-training.png new file mode 100644 index 0000000000000000000000000000000000000000..1480707c1c8567e966a1106cffe2d61888b8d297 --- /dev/null +++ b/baselines/gail/result/walker2d-training.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ffb367a14c4b226777b2e21e1cab49004ba4f6dfb8edfebc6c8483750c960e24 +size 643940 diff --git a/baselines/gail/run_mujoco.py b/baselines/gail/run_mujoco.py new file mode 100644 index 0000000000000000000000000000000000000000..e875c5ccd2d6b1d82f4ac932b6e74156fff677e7 --- /dev/null +++ b/baselines/gail/run_mujoco.py @@ -0,0 +1,239 @@ +''' +Disclaimer: this code is highly based on trpo_mpi at @openai/baselines and @openai/imitation +''' + +import argparse +import os.path as osp +import logging +from mpi4py import MPI +from tqdm import tqdm + +import numpy as np +import gym + +from baselines.gail import mlp_policy +from baselines.common import set_global_seeds, tf_util as U +from baselines.common.misc_util import boolean_flag +from baselines import bench +from baselines import logger +from baselines.gail.dataset.mujoco_dset import Mujoco_Dset +from baselines.gail.adversary import TransitionClassifier + + +def argsparser(): + parser = argparse.ArgumentParser("Tensorflow Implementation of GAIL") + parser.add_argument('--env_id', help='environment ID', default='Hopper-v2') + parser.add_argument('--seed', help='RNG seed', type=int, default=0) + parser.add_argument('--expert_path', type=str, default='data/deterministic.trpo.Hopper.0.00.npz') + parser.add_argument('--checkpoint_dir', help='the directory to save model', default='checkpoint') + parser.add_argument('--log_dir', help='the directory to save log file', default='log') + parser.add_argument('--load_model_path', help='if provided, load the model', type=str, default=None) + # Task + parser.add_argument('--task', type=str, choices=['train', 'evaluate', 'sample'], default='train') + # for evaluatation + boolean_flag(parser, 'stochastic_policy', default=False, help='use stochastic/deterministic policy to evaluate') + boolean_flag(parser, 'save_sample', default=False, help='save the trajectories or not') + # Mujoco Dataset Configuration + parser.add_argument('--traj_limitation', type=int, default=-1) + # Optimization Configuration + parser.add_argument('--g_step', help='number of steps to train policy in each epoch', type=int, default=3) + parser.add_argument('--d_step', help='number of steps to train discriminator in each epoch', type=int, default=1) + # Network Configuration (Using MLP Policy) + parser.add_argument('--policy_hidden_size', type=int, default=100) + parser.add_argument('--adversary_hidden_size', type=int, default=100) + # Algorithms Configuration + parser.add_argument('--algo', type=str, choices=['trpo', 'ppo'], default='trpo') + parser.add_argument('--max_kl', type=float, default=0.01) + parser.add_argument('--policy_entcoeff', help='entropy coefficiency of policy', type=float, default=0) + parser.add_argument('--adversary_entcoeff', help='entropy coefficiency of discriminator', type=float, default=1e-3) + # Traing Configuration + parser.add_argument('--save_per_iter', help='save model every xx iterations', type=int, default=100) + parser.add_argument('--num_timesteps', help='number of timesteps per episode', type=int, default=5e6) + # Behavior Cloning + boolean_flag(parser, 'pretrained', default=False, help='Use BC to pretrain') + parser.add_argument('--BC_max_iter', help='Max iteration for training BC', type=int, default=1e4) + return parser.parse_args() + + +def get_task_name(args): + task_name = args.algo + "_gail." + if args.pretrained: + task_name += "with_pretrained." + if args.traj_limitation != np.inf: + task_name += "transition_limitation_%d." % args.traj_limitation + task_name += args.env_id.split("-")[0] + task_name = task_name + ".g_step_" + str(args.g_step) + ".d_step_" + str(args.d_step) + \ + ".policy_entcoeff_" + str(args.policy_entcoeff) + ".adversary_entcoeff_" + str(args.adversary_entcoeff) + task_name += ".seed_" + str(args.seed) + return task_name + + +def main(args): + U.make_session(num_cpu=1).__enter__() + set_global_seeds(args.seed) + env = gym.make(args.env_id) + + def policy_fn(name, ob_space, ac_space, reuse=False): + return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, + reuse=reuse, hid_size=args.policy_hidden_size, num_hid_layers=2) + env = bench.Monitor(env, logger.get_dir() and + osp.join(logger.get_dir(), "monitor.json")) + env.seed(args.seed) + gym.logger.setLevel(logging.WARN) + task_name = get_task_name(args) + args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name) + args.log_dir = osp.join(args.log_dir, task_name) + + if args.task == 'train': + dataset = Mujoco_Dset(expert_path=args.expert_path, traj_limitation=args.traj_limitation) + reward_giver = TransitionClassifier(env, args.adversary_hidden_size, entcoeff=args.adversary_entcoeff) + train(env, + args.seed, + policy_fn, + reward_giver, + dataset, + args.algo, + args.g_step, + args.d_step, + args.policy_entcoeff, + args.num_timesteps, + args.save_per_iter, + args.checkpoint_dir, + args.log_dir, + args.pretrained, + args.BC_max_iter, + task_name + ) + elif args.task == 'evaluate': + runner(env, + policy_fn, + args.load_model_path, + timesteps_per_batch=1024, + number_trajs=10, + stochastic_policy=args.stochastic_policy, + save=args.save_sample + ) + else: + raise NotImplementedError + env.close() + + +def train(env, seed, policy_fn, reward_giver, dataset, algo, + g_step, d_step, policy_entcoeff, num_timesteps, save_per_iter, + checkpoint_dir, log_dir, pretrained, BC_max_iter, task_name=None): + + pretrained_weight = None + if pretrained and (BC_max_iter > 0): + # Pretrain with behavior cloning + from baselines.gail import behavior_clone + pretrained_weight = behavior_clone.learn(env, policy_fn, dataset, + max_iters=BC_max_iter) + + if algo == 'trpo': + from baselines.gail import trpo_mpi + # Set up for MPI seed + rank = MPI.COMM_WORLD.Get_rank() + if rank != 0: + logger.set_level(logger.DISABLED) + workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank() + set_global_seeds(workerseed) + env.seed(workerseed) + trpo_mpi.learn(env, policy_fn, reward_giver, dataset, rank, + pretrained=pretrained, pretrained_weight=pretrained_weight, + g_step=g_step, d_step=d_step, + entcoeff=policy_entcoeff, + max_timesteps=num_timesteps, + ckpt_dir=checkpoint_dir, log_dir=log_dir, + save_per_iter=save_per_iter, + timesteps_per_batch=1024, + max_kl=0.01, cg_iters=10, cg_damping=0.1, + gamma=0.995, lam=0.97, + vf_iters=5, vf_stepsize=1e-3, + task_name=task_name) + else: + raise NotImplementedError + + +def runner(env, policy_func, load_model_path, timesteps_per_batch, number_trajs, + stochastic_policy, save=False, reuse=False): + + # Setup network + # ---------------------------------------- + ob_space = env.observation_space + ac_space = env.action_space + pi = policy_func("pi", ob_space, ac_space, reuse=reuse) + U.initialize() + # Prepare for rollouts + # ---------------------------------------- + U.load_variables(load_model_path) + + obs_list = [] + acs_list = [] + len_list = [] + ret_list = [] + for _ in tqdm(range(number_trajs)): + traj = traj_1_generator(pi, env, timesteps_per_batch, stochastic=stochastic_policy) + obs, acs, ep_len, ep_ret = traj['ob'], traj['ac'], traj['ep_len'], traj['ep_ret'] + obs_list.append(obs) + acs_list.append(acs) + len_list.append(ep_len) + ret_list.append(ep_ret) + if stochastic_policy: + print('stochastic policy:') + else: + print('deterministic policy:') + if save: + filename = load_model_path.split('/')[-1] + '.' + env.spec.id + np.savez(filename, obs=np.array(obs_list), acs=np.array(acs_list), + lens=np.array(len_list), rets=np.array(ret_list)) + avg_len = sum(len_list)/len(len_list) + avg_ret = sum(ret_list)/len(ret_list) + print("Average length:", avg_len) + print("Average return:", avg_ret) + return avg_len, avg_ret + + +# Sample one trajectory (until trajectory end) +def traj_1_generator(pi, env, horizon, stochastic): + + t = 0 + ac = env.action_space.sample() # not used, just so we have the datatype + new = True # marks if we're on first timestep of an episode + + ob = env.reset() + cur_ep_ret = 0 # return in current episode + cur_ep_len = 0 # len of current episode + + # Initialize history arrays + obs = [] + rews = [] + news = [] + acs = [] + + while True: + ac, vpred = pi.act(stochastic, ob) + obs.append(ob) + news.append(new) + acs.append(ac) + + ob, rew, new, _ = env.step(ac) + rews.append(rew) + + cur_ep_ret += rew + cur_ep_len += 1 + if new or t >= horizon: + break + t += 1 + + obs = np.array(obs) + rews = np.array(rews) + news = np.array(news) + acs = np.array(acs) + traj = {"ob": obs, "rew": rews, "new": news, "ac": acs, + "ep_ret": cur_ep_ret, "ep_len": cur_ep_len} + return traj + + +if __name__ == '__main__': + args = argsparser() + main(args) diff --git a/baselines/gail/statistics.py b/baselines/gail/statistics.py new file mode 100644 index 0000000000000000000000000000000000000000..6163ea72cf80f287fe057f2501194a5ef75e6aab --- /dev/null +++ b/baselines/gail/statistics.py @@ -0,0 +1,45 @@ +''' +This code is highly based on https://github.com/carpedm20/deep-rl-tensorflow/blob/master/agents/statistic.py +''' + +import tensorflow as tf +import numpy as np + +import baselines.common.tf_util as U + + +class stats(): + + def __init__(self, scalar_keys=[], histogram_keys=[]): + self.scalar_keys = scalar_keys + self.histogram_keys = histogram_keys + self.scalar_summaries = [] + self.scalar_summaries_ph = [] + self.histogram_summaries_ph = [] + self.histogram_summaries = [] + with tf.compat.v1.variable_scope('summary'): + for k in scalar_keys: + ph = tf.compat.v1.placeholder('float32', None, name=k+'.scalar.summary') + sm = tf.compat.v1.summary.scalar(k+'.scalar.summary', ph) + self.scalar_summaries_ph.append(ph) + self.scalar_summaries.append(sm) + for k in histogram_keys: + ph = tf.compat.v1.placeholder('float32', None, name=k+'.histogram.summary') + sm = tf.compat.v1.summary.scalar(k+'.histogram.summary', ph) + self.histogram_summaries_ph.append(ph) + self.histogram_summaries.append(sm) + + self.summaries = tf.compat.v1.summary.merge(self.scalar_summaries+self.histogram_summaries) + + def add_all_summary(self, writer, values, iter): + # Note that the order of the incoming ```values``` should be the same as the that of the + # ```scalar_keys``` given in ```__init__``` + if np.sum(np.isnan(values)+0) != 0: + return + sess = U.get_session() + keys = self.scalar_summaries_ph + self.histogram_summaries_ph + feed_dict = {} + for k, v in zip(keys, values): + feed_dict.update({k: v}) + summaries_str = sess.run(self.summaries, feed_dict) + writer.add_summary(summaries_str, iter) diff --git a/baselines/gail/trpo_mpi.py b/baselines/gail/trpo_mpi.py new file mode 100644 index 0000000000000000000000000000000000000000..cb792bd1daf3ce4a543493a03ed82a942e3cc536 --- /dev/null +++ b/baselines/gail/trpo_mpi.py @@ -0,0 +1,354 @@ +''' +Disclaimer: The trpo part highly rely on trpo_mpi at @openai/baselines +''' + +import time +import os +from contextlib import contextmanager +from mpi4py import MPI +from collections import deque + +import tensorflow as tf +import numpy as np + +import baselines.common.tf_util as U +from baselines.common import explained_variance, zipsame, dataset, fmt_row +from baselines import logger +from baselines.common import colorize +from baselines.common.mpi_adam import MpiAdam +from baselines.common.cg import cg +from baselines.gail.statistics import stats + + +def traj_segment_generator(pi, env, reward_giver, horizon, stochastic): + + # Initialize state variables + t = 0 + ac = env.action_space.sample() + new = True + rew = 0.0 + true_rew = 0.0 + ob = env.reset() + + cur_ep_ret = 0 + cur_ep_len = 0 + cur_ep_true_ret = 0 + ep_true_rets = [] + ep_rets = [] + ep_lens = [] + + # Initialize history arrays + obs = np.array([ob for _ in range(horizon)]) + true_rews = np.zeros(horizon, 'float32') + rews = np.zeros(horizon, 'float32') + vpreds = np.zeros(horizon, 'float32') + news = np.zeros(horizon, 'int32') + acs = np.array([ac for _ in range(horizon)]) + prevacs = acs.copy() + + while True: + prevac = ac + ac, vpred = pi.act(stochastic, ob) + # Slight weirdness here because we need value function at time T + # before returning segment [0, T-1] so we get the correct + # terminal value + if t > 0 and t % horizon == 0: + yield {"ob": obs, "rew": rews, "vpred": vpreds, "new": news, + "ac": acs, "prevac": prevacs, "nextvpred": vpred * (1 - new), + "ep_rets": ep_rets, "ep_lens": ep_lens, "ep_true_rets": ep_true_rets} + _, vpred = pi.act(stochastic, ob) + # Be careful!!! if you change the downstream algorithm to aggregate + # several of these batches, then be sure to do a deepcopy + ep_rets = [] + ep_true_rets = [] + ep_lens = [] + i = t % horizon + obs[i] = ob + vpreds[i] = vpred + news[i] = new + acs[i] = ac + prevacs[i] = prevac + + rew = reward_giver.get_reward(ob, ac) + ob, true_rew, new, _ = env.step(ac) + rews[i] = rew + true_rews[i] = true_rew + + cur_ep_ret += rew + cur_ep_true_ret += true_rew + cur_ep_len += 1 + if new: + ep_rets.append(cur_ep_ret) + ep_true_rets.append(cur_ep_true_ret) + ep_lens.append(cur_ep_len) + cur_ep_ret = 0 + cur_ep_true_ret = 0 + cur_ep_len = 0 + ob = env.reset() + t += 1 + + +def add_vtarg_and_adv(seg, gamma, lam): + new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1 + vpred = np.append(seg["vpred"], seg["nextvpred"]) + T = len(seg["rew"]) + seg["adv"] = gaelam = np.empty(T, 'float32') + rew = seg["rew"] + lastgaelam = 0 + for t in reversed(range(T)): + nonterminal = 1-new[t+1] + delta = rew[t] + gamma * vpred[t+1] * nonterminal - vpred[t] + gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam + seg["tdlamret"] = seg["adv"] + seg["vpred"] + + +def learn(env, policy_func, reward_giver, expert_dataset, rank, + pretrained, pretrained_weight, *, + g_step, d_step, entcoeff, save_per_iter, + ckpt_dir, log_dir, timesteps_per_batch, task_name, + gamma, lam, + max_kl, cg_iters, cg_damping=1e-2, + vf_stepsize=3e-4, d_stepsize=3e-4, vf_iters=3, + max_timesteps=0, max_episodes=0, max_iters=0, + callback=None + ): + + nworkers = MPI.COMM_WORLD.Get_size() + rank = MPI.COMM_WORLD.Get_rank() + np.set_printoptions(precision=3) + # Setup losses and stuff + # ---------------------------------------- + ob_space = env.observation_space + ac_space = env.action_space + pi = policy_func("pi", ob_space, ac_space, reuse=(pretrained_weight != None)) + oldpi = policy_func("oldpi", ob_space, ac_space) + atarg = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable) + ret = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None]) # Empirical return + + ob = U.get_placeholder_cached(name="ob") + ac = pi.pdtype.sample_placeholder([None]) + + kloldnew = oldpi.pd.kl(pi.pd) + ent = pi.pd.entropy() + meankl = tf.reduce_mean(input_tensor=kloldnew) + meanent = tf.reduce_mean(input_tensor=ent) + entbonus = entcoeff * meanent + + vferr = tf.reduce_mean(input_tensor=tf.square(pi.vpred - ret)) + + ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # advantage * pnew / pold + surrgain = tf.reduce_mean(input_tensor=ratio * atarg) + + optimgain = surrgain + entbonus + losses = [optimgain, meankl, entbonus, surrgain, meanent] + loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"] + + dist = meankl + + all_var_list = pi.get_trainable_variables() + var_list = [v for v in all_var_list if v.name.startswith("pi/pol") or v.name.startswith("pi/logstd")] + vf_var_list = [v for v in all_var_list if v.name.startswith("pi/vff")] + assert len(var_list) == len(vf_var_list) + 1 + d_adam = MpiAdam(reward_giver.get_trainable_variables()) + vfadam = MpiAdam(vf_var_list) + + get_flat = U.GetFlat(var_list) + set_from_flat = U.SetFromFlat(var_list) + klgrads = tf.gradients(ys=dist, xs=var_list) + flat_tangent = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None], name="flat_tan") + shapes = [var.get_shape().as_list() for var in var_list] + start = 0 + tangents = [] + for shape in shapes: + sz = U.intprod(shape) + tangents.append(tf.reshape(flat_tangent[start:start+sz], shape)) + start += sz + gvp = tf.add_n([tf.reduce_sum(input_tensor=g*tangent) for (g, tangent) in zipsame(klgrads, tangents)]) # pylint: disable=E1111 + fvp = U.flatgrad(gvp, var_list) + + assign_old_eq_new = U.function([], [], updates=[tf.compat.v1.assign(oldv, newv) + for (oldv, newv) in zipsame(oldpi.get_variables(), pi.get_variables())]) + compute_losses = U.function([ob, ac, atarg], losses) + compute_lossandgrad = U.function([ob, ac, atarg], losses + [U.flatgrad(optimgain, var_list)]) + compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp) + compute_vflossandgrad = U.function([ob, ret], U.flatgrad(vferr, vf_var_list)) + + @contextmanager + def timed(msg): + if rank == 0: + print(colorize(msg, color='magenta')) + tstart = time.time() + yield + print(colorize("done in %.3f seconds" % (time.time() - tstart), color='magenta')) + else: + yield + + def allmean(x): + assert isinstance(x, np.ndarray) + out = np.empty_like(x) + MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM) + out /= nworkers + return out + + U.initialize() + th_init = get_flat() + MPI.COMM_WORLD.Bcast(th_init, root=0) + set_from_flat(th_init) + d_adam.sync() + vfadam.sync() + if rank == 0: + print("Init param sum", th_init.sum(), flush=True) + + # Prepare for rollouts + # ---------------------------------------- + seg_gen = traj_segment_generator(pi, env, reward_giver, timesteps_per_batch, stochastic=True) + + episodes_so_far = 0 + timesteps_so_far = 0 + iters_so_far = 0 + tstart = time.time() + lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths + rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards + true_rewbuffer = deque(maxlen=40) + + assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1 + + g_loss_stats = stats(loss_names) + d_loss_stats = stats(reward_giver.loss_name) + ep_stats = stats(["True_rewards", "Rewards", "Episode_length"]) + # if provide pretrained weight + if pretrained_weight is not None: + U.load_state(pretrained_weight, var_list=pi.get_variables()) + + while True: + if callback: callback(locals(), globals()) + if max_timesteps and timesteps_so_far >= max_timesteps: + break + elif max_episodes and episodes_so_far >= max_episodes: + break + elif max_iters and iters_so_far >= max_iters: + break + + # Save model + if rank == 0 and iters_so_far % save_per_iter == 0 and ckpt_dir is not None: + fname = os.path.join(ckpt_dir, task_name) + os.makedirs(os.path.dirname(fname), exist_ok=True) + saver = tf.compat.v1.train.Saver() + saver.save(tf.compat.v1.get_default_session(), fname) + + logger.log("********** Iteration %i ************" % iters_so_far) + + def fisher_vector_product(p): + return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p + # ------------------ Update G ------------------ + logger.log("Optimizing Policy...") + for _ in range(g_step): + with timed("sampling"): + seg = seg_gen.__next__() + add_vtarg_and_adv(seg, gamma, lam) + # ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets)) + ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"] + vpredbefore = seg["vpred"] # predicted value function before udpate + atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate + + if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy + + args = seg["ob"], seg["ac"], atarg + fvpargs = [arr[::5] for arr in args] + + assign_old_eq_new() # set old parameter values to new parameter values + with timed("computegrad"): + *lossbefore, g = compute_lossandgrad(*args) + lossbefore = allmean(np.array(lossbefore)) + g = allmean(g) + if np.allclose(g, 0): + logger.log("Got zero gradient. not updating") + else: + with timed("cg"): + stepdir = cg(fisher_vector_product, g, cg_iters=cg_iters, verbose=rank == 0) + assert np.isfinite(stepdir).all() + shs = .5*stepdir.dot(fisher_vector_product(stepdir)) + lm = np.sqrt(shs / max_kl) + # logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g)) + fullstep = stepdir / lm + expectedimprove = g.dot(fullstep) + surrbefore = lossbefore[0] + stepsize = 1.0 + thbefore = get_flat() + for _ in range(10): + thnew = thbefore + fullstep * stepsize + set_from_flat(thnew) + meanlosses = surr, kl, *_ = allmean(np.array(compute_losses(*args))) + improve = surr - surrbefore + logger.log("Expected: %.3f Actual: %.3f" % (expectedimprove, improve)) + if not np.isfinite(meanlosses).all(): + logger.log("Got non-finite value of losses -- bad!") + elif kl > max_kl * 1.5: + logger.log("violated KL constraint. shrinking step.") + elif improve < 0: + logger.log("surrogate didn't improve. shrinking step.") + else: + logger.log("Stepsize OK!") + break + stepsize *= .5 + else: + logger.log("couldn't compute a good step") + set_from_flat(thbefore) + if nworkers > 1 and iters_so_far % 20 == 0: + paramsums = MPI.COMM_WORLD.allgather((thnew.sum(), vfadam.getflat().sum())) # list of tuples + assert all(np.allclose(ps, paramsums[0]) for ps in paramsums[1:]) + with timed("vf"): + for _ in range(vf_iters): + for (mbob, mbret) in dataset.iterbatches((seg["ob"], seg["tdlamret"]), + include_final_partial_batch=False, batch_size=128): + if hasattr(pi, "ob_rms"): + pi.ob_rms.update(mbob) # update running mean/std for policy + g = allmean(compute_vflossandgrad(mbob, mbret)) + vfadam.update(g, vf_stepsize) + + g_losses = meanlosses + for (lossname, lossval) in zip(loss_names, meanlosses): + logger.record_tabular(lossname, lossval) + logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret)) + # ------------------ Update D ------------------ + logger.log("Optimizing Discriminator...") + logger.log(fmt_row(13, reward_giver.loss_name)) + ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob)) + batch_size = len(ob) // d_step + d_losses = [] # list of tuples, each of which gives the loss for a minibatch + for ob_batch, ac_batch in dataset.iterbatches((ob, ac), + include_final_partial_batch=False, + batch_size=batch_size): + ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob_batch)) + # update running mean/std for reward_giver + if hasattr(reward_giver, "obs_rms"): reward_giver.obs_rms.update(np.concatenate((ob_batch, ob_expert), 0)) + *newlosses, g = reward_giver.lossandgrad(ob_batch, ac_batch, ob_expert, ac_expert) + d_adam.update(allmean(g), d_stepsize) + d_losses.append(newlosses) + logger.log(fmt_row(13, np.mean(d_losses, axis=0))) + + lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"]) # local values + listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples + lens, rews, true_rets = map(flatten_lists, zip(*listoflrpairs)) + true_rewbuffer.extend(true_rets) + lenbuffer.extend(lens) + rewbuffer.extend(rews) + + logger.record_tabular("EpLenMean", np.mean(lenbuffer)) + logger.record_tabular("EpRewMean", np.mean(rewbuffer)) + logger.record_tabular("EpTrueRewMean", np.mean(true_rewbuffer)) + logger.record_tabular("EpThisIter", len(lens)) + episodes_so_far += len(lens) + timesteps_so_far += sum(lens) + iters_so_far += 1 + + logger.record_tabular("EpisodesSoFar", episodes_so_far) + logger.record_tabular("TimestepsSoFar", timesteps_so_far) + logger.record_tabular("TimeElapsed", time.time() - tstart) + + if rank == 0: + logger.dump_tabular() + + +def flatten_lists(listoflists): + return [el for list_ in listoflists for el in list_] diff --git a/baselines/her/README.md b/baselines/her/README.md new file mode 100644 index 0000000000000000000000000000000000000000..232b5053bda1c038c3e8f1fb3f897d2d3d68833b --- /dev/null +++ b/baselines/her/README.md @@ -0,0 +1,85 @@ +# Hindsight Experience Replay +For details on Hindsight Experience Replay (HER), please read the [paper](https://arxiv.org/abs/1707.01495). + +## How to use Hindsight Experience Replay + +### Getting started +Training an agent is very simple: +```bash +python -m baselines.run --alg=her --env=FetchReach-v1 --num_timesteps=5000 +``` +This will train a DDPG+HER agent on the `FetchReach` environment. +You should see the success rate go up quickly to `1.0`, which means that the agent achieves the +desired goal in 100% of the cases (note how HER can solve it in <5k steps - try doing that with PPO by replacing her with ppo2 :)) +The training script logs other diagnostics as well. Policy at the end of the training can be saved using `--save_path` flag, for instance: +```bash +python -m baselines.run --alg=her --env=FetchReach-v1 --num_timesteps=5000 --save_path=~/policies/her/fetchreach5k +``` + +To inspect what the agent has learned, use the `--play` flag: +```bash +python -m baselines.run --alg=her --env=FetchReach-v1 --num_timesteps=5000 --play +``` +(note `--play` can be combined with `--load_path`, which lets one load trained policies, for more results see [README.md](../../README.md)) + + +### Reproducing results +In [Plappert et al. (2018)](https://arxiv.org/abs/1802.09464), 38 trajectories were generated in parallel +(19 MPI processes, each generating computing gradients from 2 trajectories and aggregating). +To reproduce that behaviour, use +```bash +mpirun -np 19 python -m baselines.run --num_env=2 --alg=her ... +``` +This will require a machine with sufficient amount of physical CPU cores. In our experiments, +we used [Azure's D15v2 instances](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/sizes), +which have 20 physical cores. We only scheduled the experiment on 19 of those to leave some head-room on the system. + + +## Hindsight Experience Replay with Demonstrations +Using pre-recorded demonstrations to Overcome the exploration problem in HER based Reinforcement learning. +For details, please read the [paper](https://arxiv.org/pdf/1709.10089.pdf). + +### Getting started +The first step is to generate the demonstration dataset. This can be done in two ways, either by using a VR system to manipulate the arm using physical VR trackers or the simpler way is to write a script to carry out the respective task. Now some tasks can be complex and thus it would be difficult to write a hardcoded script for that task (eg. Fetch Push), but here our focus is on providing an algorithm that helps the agent to learn from demonstrations, and not on the demonstration generation paradigm itself. Thus the data collection part is left to the reader's choice. + +We provide a script for the Fetch Pick and Place task, to generate demonstrations for the Pick and Place task execute: +```bash +python experiment/data_generation/fetch_data_generation.py +``` +This outputs ```data_fetch_random_100.npz``` file which is our data file. + +To launch training with demonstrations (more technically, with behaviour cloning loss as an auxilliary loss), run the following +```bash +python -m baselines.run --alg=her --env=FetchPickAndPlace-v1 --num_timesteps=2.5e6 --demo_file=/Path/to/demo_file.npz +``` +This will train a DDPG+HER agent on the `FetchPickAndPlace` environment by using previously generated demonstration data. +To inspect what the agent has learned, use the `--play` flag as described above. + +#### Configuration +The provided configuration is for training an agent with HER without demonstrations, we need to change a few paramters for the HER algorithm to learn through demonstrations, to do that, set: + +* bc_loss: 1 - whether or not to use the behavior cloning loss as an auxilliary loss +* q_filter: 1 - whether or not a Q value filter should be used on the Actor outputs +* num_demo: 100 - number of expert demo episodes +* demo_batch_size: 128 - number of samples to be used from the demonstrations buffer, per mpi thread +* prm_loss_weight: 0.001 - Weight corresponding to the primary loss +* aux_loss_weight: 0.0078 - Weight corresponding to the auxilliary loss also called the cloning loss + +Apart from these changes the reported results also have the following configurational changes: + +* n_cycles: 20 - per epoch +* batch_size: 1024 - per mpi thread, total batch size +* random_eps: 0.1 - percentage of time a random action is taken +* noise_eps: 0.1 - std of gaussian noise added to not-completely-random actions + +These parameters can be changed either in [experiment/config.py](experiment/config.py) or passed to the command line as `--param=value`) + +### Results +Training with demonstrations helps overcome the exploration problem and achieves a faster and better convergence. The following graphs contrast the difference between training with and without demonstration data, We report the mean Q values vs Epoch and the Success Rate vs Epoch: + + +
+
+
Training results for Fetch Pick and Place task constrasting between training with and without demonstration data.
+
+ diff --git a/baselines/her/__init__.py b/baselines/her/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/her/actor_critic.py b/baselines/her/actor_critic.py new file mode 100644 index 0000000000000000000000000000000000000000..c6fe5b388c58d65374a7ec16e9e1cba33657bd40 --- /dev/null +++ b/baselines/her/actor_critic.py @@ -0,0 +1,44 @@ +import tensorflow as tf +from baselines.her.util import store_args, nn + + +class ActorCritic: + @store_args + def __init__(self, inputs_tf, dimo, dimg, dimu, max_u, o_stats, g_stats, hidden, layers, + **kwargs): + """The actor-critic network and related training code. + + Args: + inputs_tf (dict of tensors): all necessary inputs for the network: the + observation (o), the goal (g), and the action (u) + dimo (int): the dimension of the observations + dimg (int): the dimension of the goals + dimu (int): the dimension of the actions + max_u (float): the maximum magnitude of actions; action outputs will be scaled + accordingly + o_stats (baselines.her.Normalizer): normalizer for observations + g_stats (baselines.her.Normalizer): normalizer for goals + hidden (int): number of hidden units that should be used in hidden layers + layers (int): number of hidden layers + """ + self.o_tf = inputs_tf['o'] + self.g_tf = inputs_tf['g'] + self.u_tf = inputs_tf['u'] + + # Prepare inputs for actor and critic. + o = self.o_stats.normalize(self.o_tf) + g = self.g_stats.normalize(self.g_tf) + input_pi = tf.concat(axis=1, values=[o, g]) # for actor + + # Networks. + with tf.compat.v1.variable_scope('pi'): + self.pi_tf = self.max_u * tf.tanh(nn( + input_pi, [self.hidden] * self.layers + [self.dimu])) + with tf.compat.v1.variable_scope('Q'): + # for policy training + input_Q = tf.concat(axis=1, values=[o, g, self.pi_tf / self.max_u]) + self.Q_pi_tf = nn(input_Q, [self.hidden] * self.layers + [1]) + # for critic training + input_Q = tf.concat(axis=1, values=[o, g, self.u_tf / self.max_u]) + self._input_Q = input_Q # exposed for tests + self.Q_tf = nn(input_Q, [self.hidden] * self.layers + [1], reuse=True) diff --git a/baselines/her/ddpg.py b/baselines/her/ddpg.py new file mode 100644 index 0000000000000000000000000000000000000000..f054839d707e8879e27501ca706b474b0ff61736 --- /dev/null +++ b/baselines/her/ddpg.py @@ -0,0 +1,448 @@ +from collections import OrderedDict + +import numpy as np +import tensorflow as tf +from tensorflow.contrib.staging import StagingArea + +from baselines import logger +from baselines.her.util import ( + import_function, store_args, flatten_grads, transitions_in_episode_batch, convert_episode_to_batch_major) +from baselines.her.normalizer import Normalizer +from baselines.her.replay_buffer import ReplayBuffer +from baselines.common.mpi_adam import MpiAdam +from baselines.common import tf_util + + +def dims_to_shapes(input_dims): + return {key: tuple([val]) if val > 0 else tuple() for key, val in input_dims.items()} + + +global DEMO_BUFFER #buffer for demonstrations + +class DDPG(object): + @store_args + def __init__(self, input_dims, buffer_size, hidden, layers, network_class, polyak, batch_size, + Q_lr, pi_lr, norm_eps, norm_clip, max_u, action_l2, clip_obs, scope, T, + rollout_batch_size, subtract_goals, relative_goals, clip_pos_returns, clip_return, + bc_loss, q_filter, num_demo, demo_batch_size, prm_loss_weight, aux_loss_weight, + sample_transitions, gamma, reuse=False, **kwargs): + """Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER). + Added functionality to use demonstrations for training to Overcome exploration problem. + + Args: + input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the + actions (u) + buffer_size (int): number of transitions that are stored in the replay buffer + hidden (int): number of units in the hidden layers + layers (int): number of hidden layers + network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic') + polyak (float): coefficient for Polyak-averaging of the target network + batch_size (int): batch size for training + Q_lr (float): learning rate for the Q (critic) network + pi_lr (float): learning rate for the pi (actor) network + norm_eps (float): a small value used in the normalizer to avoid numerical instabilities + norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip] + max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u] + action_l2 (float): coefficient for L2 penalty on the actions + clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs] + scope (str): the scope used for the TensorFlow graph + T (int): the time horizon for rollouts + rollout_batch_size (int): number of parallel rollouts per DDPG agent + subtract_goals (function): function that subtracts goals from each other + relative_goals (boolean): whether or not relative goals should be fed into the network + clip_pos_returns (boolean): whether or not positive returns should be clipped + clip_return (float): clip returns to be in [-clip_return, clip_return] + sample_transitions (function) function that samples from the replay buffer + gamma (float): gamma used for Q learning updates + reuse (boolean): whether or not the networks should be reused + bc_loss: whether or not the behavior cloning loss should be used as an auxilliary loss + q_filter: whether or not a filter on the q value update should be used when training with demonstartions + num_demo: Number of episodes in to be used in the demonstration buffer + demo_batch_size: number of samples to be used from the demonstrations buffer, per mpi thread + prm_loss_weight: Weight corresponding to the primary loss + aux_loss_weight: Weight corresponding to the auxilliary loss also called the cloning loss + """ + if self.clip_return is None: + self.clip_return = np.inf + + self.create_actor_critic = import_function(self.network_class) + + input_shapes = dims_to_shapes(self.input_dims) + self.dimo = self.input_dims['o'] + self.dimg = self.input_dims['g'] + self.dimu = self.input_dims['u'] + + # Prepare staging area for feeding data to the model. + stage_shapes = OrderedDict() + for key in sorted(self.input_dims.keys()): + if key.startswith('info_'): + continue + stage_shapes[key] = (None, *input_shapes[key]) + for key in ['o', 'g']: + stage_shapes[key + '_2'] = stage_shapes[key] + stage_shapes['r'] = (None,) + self.stage_shapes = stage_shapes + + # Create network. + with tf.compat.v1.variable_scope(self.scope): + self.staging_tf = StagingArea( + dtypes=[tf.float32 for _ in self.stage_shapes.keys()], + shapes=list(self.stage_shapes.values())) + self.buffer_ph_tf = [ + tf.compat.v1.placeholder(tf.float32, shape=shape) for shape in self.stage_shapes.values()] + self.stage_op = self.staging_tf.put(self.buffer_ph_tf) + + self._create_network(reuse=reuse) + + # Configure the replay buffer. + buffer_shapes = {key: (self.T-1 if key != 'o' else self.T, *input_shapes[key]) + for key, val in input_shapes.items()} + buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg) + buffer_shapes['ag'] = (self.T, self.dimg) + + buffer_size = (self.buffer_size // self.rollout_batch_size) * self.rollout_batch_size + self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions) + + global DEMO_BUFFER + DEMO_BUFFER = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions) #initialize the demo buffer; in the same way as the primary data buffer + + def _random_action(self, n): + return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu)) + + def _preprocess_og(self, o, ag, g): + if self.relative_goals: + g_shape = g.shape + g = g.reshape(-1, self.dimg) + ag = ag.reshape(-1, self.dimg) + g = self.subtract_goals(g, ag) + g = g.reshape(*g_shape) + o = np.clip(o, -self.clip_obs, self.clip_obs) + g = np.clip(g, -self.clip_obs, self.clip_obs) + return o, g + + def step(self, obs): + actions = self.get_actions(obs['observation'], obs['achieved_goal'], obs['desired_goal']) + return actions, None, None, None + + + def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False, + compute_Q=False): + o, g = self._preprocess_og(o, ag, g) + policy = self.target if use_target_net else self.main + # values to compute + vals = [policy.pi_tf] + if compute_Q: + vals += [policy.Q_pi_tf] + # feed + feed = { + policy.o_tf: o.reshape(-1, self.dimo), + policy.g_tf: g.reshape(-1, self.dimg), + policy.u_tf: np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32) + } + + ret = self.sess.run(vals, feed_dict=feed) + # action postprocessing + u = ret[0] + noise = noise_eps * self.max_u * np.random.randn(*u.shape) # gaussian noise + u += noise + u = np.clip(u, -self.max_u, self.max_u) + u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (self._random_action(u.shape[0]) - u) # eps-greedy + if u.shape[0] == 1: + u = u[0] + u = u.copy() + ret[0] = u + + if len(ret) == 1: + return ret[0] + else: + return ret + + def init_demo_buffer(self, demoDataFile, update_stats=True): #function that initializes the demo buffer + + demoData = np.load(demoDataFile) #load the demonstration data from data file + info_keys = [key.replace('info_', '') for key in self.input_dims.keys() if key.startswith('info_')] + info_values = [np.empty((self.T - 1, 1, self.input_dims['info_' + key]), np.float32) for key in info_keys] + + demo_data_obs = demoData['obs'] + demo_data_acs = demoData['acs'] + demo_data_info = demoData['info'] + + for epsd in range(self.num_demo): # we initialize the whole demo buffer at the start of the training + obs, acts, goals, achieved_goals = [], [] ,[] ,[] + i = 0 + for transition in range(self.T - 1): + obs.append([demo_data_obs[epsd][transition].get('observation')]) + acts.append([demo_data_acs[epsd][transition]]) + goals.append([demo_data_obs[epsd][transition].get('desired_goal')]) + achieved_goals.append([demo_data_obs[epsd][transition].get('achieved_goal')]) + for idx, key in enumerate(info_keys): + info_values[idx][transition, i] = demo_data_info[epsd][transition][key] + + + obs.append([demo_data_obs[epsd][self.T - 1].get('observation')]) + achieved_goals.append([demo_data_obs[epsd][self.T - 1].get('achieved_goal')]) + + episode = dict(o=obs, + u=acts, + g=goals, + ag=achieved_goals) + for key, value in zip(info_keys, info_values): + episode['info_{}'.format(key)] = value + + episode = convert_episode_to_batch_major(episode) + global DEMO_BUFFER + DEMO_BUFFER.store_episode(episode) # create the observation dict and append them into the demonstration buffer + logger.debug("Demo buffer size currently ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size + + if update_stats: + # add transitions to normalizer to normalize the demo data as well + episode['o_2'] = episode['o'][:, 1:, :] + episode['ag_2'] = episode['ag'][:, 1:, :] + num_normalizing_transitions = transitions_in_episode_batch(episode) + transitions = self.sample_transitions(episode, num_normalizing_transitions) + + o, g, ag = transitions['o'], transitions['g'], transitions['ag'] + transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g) + # No need to preprocess the o_2 and g_2 since this is only used for stats + + self.o_stats.update(transitions['o']) + self.g_stats.update(transitions['g']) + + self.o_stats.recompute_stats() + self.g_stats.recompute_stats() + episode.clear() + + logger.info("Demo buffer size: ", DEMO_BUFFER.get_current_size()) #print out the demonstration buffer size + + def store_episode(self, episode_batch, update_stats=True): + """ + episode_batch: array of batch_size x (T or T+1) x dim_key + 'o' is of size T+1, others are of size T + """ + + self.buffer.store_episode(episode_batch) + + if update_stats: + # add transitions to normalizer + episode_batch['o_2'] = episode_batch['o'][:, 1:, :] + episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :] + num_normalizing_transitions = transitions_in_episode_batch(episode_batch) + transitions = self.sample_transitions(episode_batch, num_normalizing_transitions) + + o, g, ag = transitions['o'], transitions['g'], transitions['ag'] + transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g) + # No need to preprocess the o_2 and g_2 since this is only used for stats + + self.o_stats.update(transitions['o']) + self.g_stats.update(transitions['g']) + + self.o_stats.recompute_stats() + self.g_stats.recompute_stats() + + def get_current_buffer_size(self): + return self.buffer.get_current_size() + + def _sync_optimizers(self): + self.Q_adam.sync() + self.pi_adam.sync() + + def _grads(self): + # Avoid feed_dict here for performance! + critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run([ + self.Q_loss_tf, + self.main.Q_pi_tf, + self.Q_grad_tf, + self.pi_grad_tf + ]) + return critic_loss, actor_loss, Q_grad, pi_grad + + def _update(self, Q_grad, pi_grad): + self.Q_adam.update(Q_grad, self.Q_lr) + self.pi_adam.update(pi_grad, self.pi_lr) + + def sample_batch(self): + if self.bc_loss: #use demonstration buffer to sample as well if bc_loss flag is set TRUE + transitions = self.buffer.sample(self.batch_size - self.demo_batch_size) + global DEMO_BUFFER + transitions_demo = DEMO_BUFFER.sample(self.demo_batch_size) #sample from the demo buffer + for k, values in transitions_demo.items(): + rolloutV = transitions[k].tolist() + for v in values: + rolloutV.append(v.tolist()) + transitions[k] = np.array(rolloutV) + else: + transitions = self.buffer.sample(self.batch_size) #otherwise only sample from primary buffer + + o, o_2, g = transitions['o'], transitions['o_2'], transitions['g'] + ag, ag_2 = transitions['ag'], transitions['ag_2'] + transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g) + transitions['o_2'], transitions['g_2'] = self._preprocess_og(o_2, ag_2, g) + + transitions_batch = [transitions[key] for key in self.stage_shapes.keys()] + return transitions_batch + + def stage_batch(self, batch=None): + if batch is None: + batch = self.sample_batch() + assert len(self.buffer_ph_tf) == len(batch) + self.sess.run(self.stage_op, feed_dict=dict(zip(self.buffer_ph_tf, batch))) + + def train(self, stage=True): + if stage: + self.stage_batch() + critic_loss, actor_loss, Q_grad, pi_grad = self._grads() + self._update(Q_grad, pi_grad) + return critic_loss, actor_loss + + def _init_target_net(self): + self.sess.run(self.init_target_net_op) + + def update_target_net(self): + self.sess.run(self.update_target_net_op) + + def clear_buffer(self): + self.buffer.clear_buffer() + + def _vars(self, scope): + res = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, scope=self.scope + '/' + scope) + assert len(res) > 0 + return res + + def _global_vars(self, scope): + res = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope=self.scope + '/' + scope) + return res + + def _create_network(self, reuse=False): + logger.info("Creating a DDPG agent with action space %d x %s..." % (self.dimu, self.max_u)) + self.sess = tf_util.get_session() + + # running averages + with tf.compat.v1.variable_scope('o_stats') as vs: + if reuse: + vs.reuse_variables() + self.o_stats = Normalizer(self.dimo, self.norm_eps, self.norm_clip, sess=self.sess) + with tf.compat.v1.variable_scope('g_stats') as vs: + if reuse: + vs.reuse_variables() + self.g_stats = Normalizer(self.dimg, self.norm_eps, self.norm_clip, sess=self.sess) + + # mini-batch sampling. + batch = self.staging_tf.get() + batch_tf = OrderedDict([(key, batch[i]) + for i, key in enumerate(self.stage_shapes.keys())]) + batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1]) + + #choose only the demo buffer samples + mask = np.concatenate((np.zeros(self.batch_size - self.demo_batch_size), np.ones(self.demo_batch_size)), axis = 0) + + # networks + with tf.compat.v1.variable_scope('main') as vs: + if reuse: + vs.reuse_variables() + self.main = self.create_actor_critic(batch_tf, net_type='main', **self.__dict__) + vs.reuse_variables() + with tf.compat.v1.variable_scope('target') as vs: + if reuse: + vs.reuse_variables() + target_batch_tf = batch_tf.copy() + target_batch_tf['o'] = batch_tf['o_2'] + target_batch_tf['g'] = batch_tf['g_2'] + self.target = self.create_actor_critic( + target_batch_tf, net_type='target', **self.__dict__) + vs.reuse_variables() + assert len(self._vars("main")) == len(self._vars("target")) + + # loss functions + target_Q_pi_tf = self.target.Q_pi_tf + clip_range = (-self.clip_return, 0. if self.clip_pos_returns else np.inf) + target_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range) + self.Q_loss_tf = tf.reduce_mean(input_tensor=tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf)) + + if self.bc_loss ==1 and self.q_filter == 1 : # train with demonstrations and use bc_loss and q_filter both + maskMain = tf.reshape(tf.boolean_mask(tensor=self.main.Q_tf > self.main.Q_pi_tf, mask=mask), [-1]) #where is the demonstrator action better than actor action according to the critic? choose those samples only + #define the cloning loss on the actor's actions only on the samples which adhere to the above masks + self.cloning_loss_tf = tf.reduce_sum(input_tensor=tf.square(tf.boolean_mask(tensor=tf.boolean_mask(tensor=(self.main.pi_tf), mask=mask), mask=maskMain, axis=0) - tf.boolean_mask(tensor=tf.boolean_mask(tensor=(batch_tf['u']), mask=mask), mask=maskMain, axis=0))) + self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(input_tensor=self.main.Q_pi_tf) #primary loss scaled by it's respective weight prm_loss_weight + self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(input_tensor=tf.square(self.main.pi_tf / self.max_u)) #L2 loss on action values scaled by the same weight prm_loss_weight + self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf #adding the cloning loss to the actor loss as an auxilliary loss scaled by its weight aux_loss_weight + + elif self.bc_loss == 1 and self.q_filter == 0: # train with demonstrations without q_filter + self.cloning_loss_tf = tf.reduce_sum(input_tensor=tf.square(tf.boolean_mask(tensor=(self.main.pi_tf), mask=mask) - tf.boolean_mask(tensor=(batch_tf['u']), mask=mask))) + self.pi_loss_tf = -self.prm_loss_weight * tf.reduce_mean(input_tensor=self.main.Q_pi_tf) + self.pi_loss_tf += self.prm_loss_weight * self.action_l2 * tf.reduce_mean(input_tensor=tf.square(self.main.pi_tf / self.max_u)) + self.pi_loss_tf += self.aux_loss_weight * self.cloning_loss_tf + + else: #If not training with demonstrations + self.pi_loss_tf = -tf.reduce_mean(input_tensor=self.main.Q_pi_tf) + self.pi_loss_tf += self.action_l2 * tf.reduce_mean(input_tensor=tf.square(self.main.pi_tf / self.max_u)) + + Q_grads_tf = tf.gradients(ys=self.Q_loss_tf, xs=self._vars('main/Q')) + pi_grads_tf = tf.gradients(ys=self.pi_loss_tf, xs=self._vars('main/pi')) + assert len(self._vars('main/Q')) == len(Q_grads_tf) + assert len(self._vars('main/pi')) == len(pi_grads_tf) + self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q')) + self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi')) + self.Q_grad_tf = flatten_grads(grads=Q_grads_tf, var_list=self._vars('main/Q')) + self.pi_grad_tf = flatten_grads(grads=pi_grads_tf, var_list=self._vars('main/pi')) + + # optimizers + self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False) + self.pi_adam = MpiAdam(self._vars('main/pi'), scale_grad_by_procs=False) + + # polyak averaging + self.main_vars = self._vars('main/Q') + self._vars('main/pi') + self.target_vars = self._vars('target/Q') + self._vars('target/pi') + self.stats_vars = self._global_vars('o_stats') + self._global_vars('g_stats') + self.init_target_net_op = list( + map(lambda v: v[0].assign(v[1]), zip(self.target_vars, self.main_vars))) + self.update_target_net_op = list( + map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(self.target_vars, self.main_vars))) + + # initialize all variables + tf.compat.v1.variables_initializer(self._global_vars('')).run() + self._sync_optimizers() + self._init_target_net() + + def logs(self, prefix=''): + logs = [] + logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))] + logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))] + logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))] + logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))] + + if prefix != '' and not prefix.endswith('/'): + return [(prefix + '/' + key, val) for key, val in logs] + else: + return logs + + def __getstate__(self): + """Our policies can be loaded from pkl, but after unpickling you cannot continue training. + """ + excluded_subnames = ['_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats', + 'main', 'target', 'lock', 'env', 'sample_transitions', + 'stage_shapes', 'create_actor_critic'] + + state = {k: v for k, v in self.__dict__.items() if all([not subname in k for subname in excluded_subnames])} + state['buffer_size'] = self.buffer_size + state['tf'] = self.sess.run([x for x in self._global_vars('') if 'buffer' not in x.name]) + return state + + def __setstate__(self, state): + if 'sample_transitions' not in state: + # We don't need this for playing the policy. + state['sample_transitions'] = None + + self.__init__(**state) + # set up stats (they are overwritten in __init__) + for k, v in state.items(): + if k[-6:] == '_stats': + self.__dict__[k] = v + # load TF variables + vars = [x for x in self._global_vars('') if 'buffer' not in x.name] + assert(len(vars) == len(state["tf"])) + node = [tf.compat.v1.assign(var, val) for var, val in zip(vars, state["tf"])] + self.sess.run(node) + + def save(self, save_path): + tf_util.save_variables(save_path) + diff --git a/baselines/her/experiment/__init__.py b/baselines/her/experiment/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/her/experiment/config.py b/baselines/her/experiment/config.py new file mode 100644 index 0000000000000000000000000000000000000000..6370505a844783a36c6866e70cf3b42fe9c23936 --- /dev/null +++ b/baselines/her/experiment/config.py @@ -0,0 +1,201 @@ +import os +import numpy as np +import gym + +from baselines import logger +from baselines.her.ddpg import DDPG +from baselines.her.her_sampler import make_sample_her_transitions +from baselines.bench.monitor import Monitor + +DEFAULT_ENV_PARAMS = { + 'FetchReach-v1': { + 'n_cycles': 10, + }, +} + + +DEFAULT_PARAMS = { + # env + 'max_u': 1., # max absolute value of actions on different coordinates + # ddpg + 'layers': 3, # number of layers in the critic/actor networks + 'hidden': 256, # number of neurons in each hidden layers + 'network_class': 'baselines.her.actor_critic:ActorCritic', + 'Q_lr': 0.001, # critic learning rate + 'pi_lr': 0.001, # actor learning rate + 'buffer_size': int(1E6), # for experience replay + 'polyak': 0.95, # polyak averaging coefficient + 'action_l2': 1.0, # quadratic penalty on actions (before rescaling by max_u) + 'clip_obs': 200., + 'scope': 'ddpg', # can be tweaked for testing + 'relative_goals': False, + # training + 'n_cycles': 50, # per epoch + 'rollout_batch_size': 2, # per mpi thread + 'n_batches': 40, # training batches per cycle + 'batch_size': 256, # per mpi thread, measured in transitions and reduced to even multiple of chunk_length. + 'n_test_rollouts': 10, # number of test rollouts per epoch, each consists of rollout_batch_size rollouts + 'test_with_polyak': False, # run test episodes with the target network + # exploration + 'random_eps': 0.3, # percentage of time a random action is taken + 'noise_eps': 0.2, # std of gaussian noise added to not-completely-random actions as a percentage of max_u + # HER + 'replay_strategy': 'future', # supported modes: future, none + 'replay_k': 4, # number of additional goals used for replay, only used if off_policy_data=future + # normalization + 'norm_eps': 0.01, # epsilon used for observation normalization + 'norm_clip': 5, # normalized observations are cropped to this values + + 'bc_loss': 0, # whether or not to use the behavior cloning loss as an auxilliary loss + 'q_filter': 0, # whether or not a Q value filter should be used on the Actor outputs + 'num_demo': 100, # number of expert demo episodes + 'demo_batch_size': 128, #number of samples to be used from the demonstrations buffer, per mpi thread 128/1024 or 32/256 + 'prm_loss_weight': 0.001, #Weight corresponding to the primary loss + 'aux_loss_weight': 0.0078, #Weight corresponding to the auxilliary loss also called the cloning loss +} + + +CACHED_ENVS = {} + + +def cached_make_env(make_env): + """ + Only creates a new environment from the provided function if one has not yet already been + created. This is useful here because we need to infer certain properties of the env, e.g. + its observation and action spaces, without any intend of actually using it. + """ + if make_env not in CACHED_ENVS: + env = make_env() + CACHED_ENVS[make_env] = env + return CACHED_ENVS[make_env] + + +def prepare_params(kwargs): + # DDPG params + ddpg_params = dict() + env_name = kwargs['env_name'] + + def make_env(subrank=None): + env = gym.make(env_name) + if subrank is not None and logger.get_dir() is not None: + try: + from mpi4py import MPI + mpi_rank = MPI.COMM_WORLD.Get_rank() + except ImportError: + MPI = None + mpi_rank = 0 + logger.warn('Running with a single MPI process. This should work, but the results may differ from the ones publshed in Plappert et al.') + + max_episode_steps = env._max_episode_steps + env = Monitor(env, + os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(subrank)), + allow_early_resets=True) + # hack to re-expose _max_episode_steps (ideally should replace reliance on it downstream) + env = gym.wrappers.TimeLimit(env, max_episode_steps=max_episode_steps) + return env + + kwargs['make_env'] = make_env + tmp_env = cached_make_env(kwargs['make_env']) + assert hasattr(tmp_env, '_max_episode_steps') + kwargs['T'] = tmp_env._max_episode_steps + + kwargs['max_u'] = np.array(kwargs['max_u']) if isinstance(kwargs['max_u'], list) else kwargs['max_u'] + kwargs['gamma'] = 1. - 1. / kwargs['T'] + if 'lr' in kwargs: + kwargs['pi_lr'] = kwargs['lr'] + kwargs['Q_lr'] = kwargs['lr'] + del kwargs['lr'] + for name in ['buffer_size', 'hidden', 'layers', + 'network_class', + 'polyak', + 'batch_size', 'Q_lr', 'pi_lr', + 'norm_eps', 'norm_clip', 'max_u', + 'action_l2', 'clip_obs', 'scope', 'relative_goals']: + ddpg_params[name] = kwargs[name] + kwargs['_' + name] = kwargs[name] + del kwargs[name] + kwargs['ddpg_params'] = ddpg_params + + return kwargs + + +def log_params(params, logger=logger): + for key in sorted(params.keys()): + logger.info('{}: {}'.format(key, params[key])) + + +def configure_her(params): + env = cached_make_env(params['make_env']) + env.reset() + + def reward_fun(ag_2, g, info): # vectorized + return env.compute_reward(achieved_goal=ag_2, desired_goal=g, info=info) + + # Prepare configuration for HER. + her_params = { + 'reward_fun': reward_fun, + } + for name in ['replay_strategy', 'replay_k']: + her_params[name] = params[name] + params['_' + name] = her_params[name] + del params[name] + sample_her_transitions = make_sample_her_transitions(**her_params) + + return sample_her_transitions + + +def simple_goal_subtract(a, b): + assert a.shape == b.shape + return a - b + + +def configure_ddpg(dims, params, reuse=False, use_mpi=True, clip_return=True): + sample_her_transitions = configure_her(params) + # Extract relevant parameters. + gamma = params['gamma'] + rollout_batch_size = params['rollout_batch_size'] + ddpg_params = params['ddpg_params'] + + input_dims = dims.copy() + + # DDPG agent + env = cached_make_env(params['make_env']) + env.reset() + ddpg_params.update({'input_dims': input_dims, # agent takes an input observations + 'T': params['T'], + 'clip_pos_returns': True, # clip positive returns + 'clip_return': (1. / (1. - gamma)) if clip_return else np.inf, # max abs of return + 'rollout_batch_size': rollout_batch_size, + 'subtract_goals': simple_goal_subtract, + 'sample_transitions': sample_her_transitions, + 'gamma': gamma, + 'bc_loss': params['bc_loss'], + 'q_filter': params['q_filter'], + 'num_demo': params['num_demo'], + 'demo_batch_size': params['demo_batch_size'], + 'prm_loss_weight': params['prm_loss_weight'], + 'aux_loss_weight': params['aux_loss_weight'], + }) + ddpg_params['info'] = { + 'env_name': params['env_name'], + } + policy = DDPG(reuse=reuse, **ddpg_params, use_mpi=use_mpi) + return policy + + +def configure_dims(params): + env = cached_make_env(params['make_env']) + env.reset() + obs, _, _, info = env.step(env.action_space.sample()) + + dims = { + 'o': obs['observation'].shape[0], + 'u': env.action_space.shape[0], + 'g': obs['desired_goal'].shape[0], + } + for key, value in info.items(): + value = np.array(value) + if value.ndim == 0: + value = value.reshape(1) + dims['info_{}'.format(key)] = value.shape[0] + return dims diff --git a/baselines/her/experiment/data_generation/fetch_data_generation.py b/baselines/her/experiment/data_generation/fetch_data_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..0a0a755db120e86439770a8a52715ba6029e2dc7 --- /dev/null +++ b/baselines/her/experiment/data_generation/fetch_data_generation.py @@ -0,0 +1,126 @@ +import gym +import numpy as np + + +"""Data generation for the case of a single block pick and place in Fetch Env""" + +actions = [] +observations = [] +infos = [] + +def main(): + env = gym.make('FetchPickAndPlace-v1') + numItr = 100 + initStateSpace = "random" + env.reset() + print("Reset!") + while len(actions) < numItr: + obs = env.reset() + print("ITERATION NUMBER ", len(actions)) + goToGoal(env, obs) + + + fileName = "data_fetch" + fileName += "_" + initStateSpace + fileName += "_" + str(numItr) + fileName += ".npz" + + np.savez_compressed(fileName, acs=actions, obs=observations, info=infos) # save the file + +def goToGoal(env, lastObs): + + goal = lastObs['desired_goal'] + objectPos = lastObs['observation'][3:6] + object_rel_pos = lastObs['observation'][6:9] + episodeAcs = [] + episodeObs = [] + episodeInfo = [] + + object_oriented_goal = object_rel_pos.copy() + object_oriented_goal[2] += 0.03 # first make the gripper go slightly above the object + + timeStep = 0 #count the total number of timesteps + episodeObs.append(lastObs) + + while np.linalg.norm(object_oriented_goal) >= 0.005 and timeStep <= env._max_episode_steps: + env.render() + action = [0, 0, 0, 0] + object_oriented_goal = object_rel_pos.copy() + object_oriented_goal[2] += 0.03 + + for i in range(len(object_oriented_goal)): + action[i] = object_oriented_goal[i]*6 + + action[len(action)-1] = 0.05 #open + + obsDataNew, reward, done, info = env.step(action) + timeStep += 1 + + episodeAcs.append(action) + episodeInfo.append(info) + episodeObs.append(obsDataNew) + + objectPos = obsDataNew['observation'][3:6] + object_rel_pos = obsDataNew['observation'][6:9] + + while np.linalg.norm(object_rel_pos) >= 0.005 and timeStep <= env._max_episode_steps : + env.render() + action = [0, 0, 0, 0] + for i in range(len(object_rel_pos)): + action[i] = object_rel_pos[i]*6 + + action[len(action)-1] = -0.005 + + obsDataNew, reward, done, info = env.step(action) + timeStep += 1 + + episodeAcs.append(action) + episodeInfo.append(info) + episodeObs.append(obsDataNew) + + objectPos = obsDataNew['observation'][3:6] + object_rel_pos = obsDataNew['observation'][6:9] + + + while np.linalg.norm(goal - objectPos) >= 0.01 and timeStep <= env._max_episode_steps : + env.render() + action = [0, 0, 0, 0] + for i in range(len(goal - objectPos)): + action[i] = (goal - objectPos)[i]*6 + + action[len(action)-1] = -0.005 + + obsDataNew, reward, done, info = env.step(action) + timeStep += 1 + + episodeAcs.append(action) + episodeInfo.append(info) + episodeObs.append(obsDataNew) + + objectPos = obsDataNew['observation'][3:6] + object_rel_pos = obsDataNew['observation'][6:9] + + while True: #limit the number of timesteps in the episode to a fixed duration + env.render() + action = [0, 0, 0, 0] + action[len(action)-1] = -0.005 # keep the gripper closed + + obsDataNew, reward, done, info = env.step(action) + timeStep += 1 + + episodeAcs.append(action) + episodeInfo.append(info) + episodeObs.append(obsDataNew) + + objectPos = obsDataNew['observation'][3:6] + object_rel_pos = obsDataNew['observation'][6:9] + + if timeStep >= env._max_episode_steps: break + + actions.append(episodeAcs) + observations.append(episodeObs) + infos.append(episodeInfo) + + +if __name__ == "__main__": + main() diff --git a/baselines/her/experiment/play.py b/baselines/her/experiment/play.py new file mode 100644 index 0000000000000000000000000000000000000000..8989c9277ecd7cdf3781df1d90a4d3b4e7b43099 --- /dev/null +++ b/baselines/her/experiment/play.py @@ -0,0 +1,61 @@ +# DEPRECATED, use --play flag to baselines.run instead +import click +import numpy as np +import pickle + +from baselines import logger +from baselines.common import set_global_seeds +import baselines.her.experiment.config as config +from baselines.her.rollout import RolloutWorker + + +@click.command() +@click.argument('policy_file', type=str) +@click.option('--seed', type=int, default=0) +@click.option('--n_test_rollouts', type=int, default=10) +@click.option('--render', type=int, default=1) +def main(policy_file, seed, n_test_rollouts, render): + set_global_seeds(seed) + + # Load policy. + with open(policy_file, 'rb') as f: + policy = pickle.load(f) + env_name = policy.info['env_name'] + + # Prepare params. + params = config.DEFAULT_PARAMS + if env_name in config.DEFAULT_ENV_PARAMS: + params.update(config.DEFAULT_ENV_PARAMS[env_name]) # merge env-specific parameters in + params['env_name'] = env_name + params = config.prepare_params(params) + config.log_params(params, logger=logger) + + dims = config.configure_dims(params) + + eval_params = { + 'exploit': True, + 'use_target_net': params['test_with_polyak'], + 'compute_Q': True, + 'rollout_batch_size': 1, + 'render': bool(render), + } + + for name in ['T', 'gamma', 'noise_eps', 'random_eps']: + eval_params[name] = params[name] + + evaluator = RolloutWorker(params['make_env'], policy, dims, logger, **eval_params) + evaluator.seed(seed) + + # Run evaluation. + evaluator.clear_history() + for _ in range(n_test_rollouts): + evaluator.generate_rollouts() + + # record logs + for key, val in evaluator.logs('test'): + logger.record_tabular(key, np.mean(val)) + logger.dump_tabular() + + +if __name__ == '__main__': + main() diff --git a/baselines/her/experiment/plot.py b/baselines/her/experiment/plot.py new file mode 100644 index 0000000000000000000000000000000000000000..542de0e1ac5a518d824addc30ec3271ea4f5656a --- /dev/null +++ b/baselines/her/experiment/plot.py @@ -0,0 +1,120 @@ +# DEPRECATED, use baselines.common.plot_util instead + +import os +import matplotlib.pyplot as plt +import numpy as np +import json +import seaborn as sns; sns.set() +import glob2 +import argparse + + +def smooth_reward_curve(x, y): + halfwidth = int(np.ceil(len(x) / 60)) # Halfwidth of our smoothing convolution + k = halfwidth + xsmoo = x + ysmoo = np.convolve(y, np.ones(2 * k + 1), mode='same') / np.convolve(np.ones_like(y), np.ones(2 * k + 1), + mode='same') + return xsmoo, ysmoo + + +def load_results(file): + if not os.path.exists(file): + return None + with open(file, 'r') as f: + lines = [line for line in f] + if len(lines) < 2: + return None + keys = [name.strip() for name in lines[0].split(',')] + data = np.genfromtxt(file, delimiter=',', skip_header=1, filling_values=0.) + if data.ndim == 1: + data = data.reshape(1, -1) + assert data.ndim == 2 + assert data.shape[-1] == len(keys) + result = {} + for idx, key in enumerate(keys): + result[key] = data[:, idx] + return result + + +def pad(xs, value=np.nan): + maxlen = np.max([len(x) for x in xs]) + + padded_xs = [] + for x in xs: + if x.shape[0] >= maxlen: + padded_xs.append(x) + + padding = np.ones((maxlen - x.shape[0],) + x.shape[1:]) * value + x_padded = np.concatenate([x, padding], axis=0) + assert x_padded.shape[1:] == x.shape[1:] + assert x_padded.shape[0] == maxlen + padded_xs.append(x_padded) + return np.array(padded_xs) + + +parser = argparse.ArgumentParser() +parser.add_argument('dir', type=str) +parser.add_argument('--smooth', type=int, default=1) +args = parser.parse_args() + +# Load all data. +data = {} +paths = [os.path.abspath(os.path.join(path, '..')) for path in glob2.glob(os.path.join(args.dir, '**', 'progress.csv'))] +for curr_path in paths: + if not os.path.isdir(curr_path): + continue + results = load_results(os.path.join(curr_path, 'progress.csv')) + if not results: + print('skipping {}'.format(curr_path)) + continue + print('loading {} ({})'.format(curr_path, len(results['epoch']))) + with open(os.path.join(curr_path, 'params.json'), 'r') as f: + params = json.load(f) + + success_rate = np.array(results['test/success_rate']) + epoch = np.array(results['epoch']) + 1 + env_id = params['env_name'] + replay_strategy = params['replay_strategy'] + + if replay_strategy == 'future': + config = 'her' + else: + config = 'ddpg' + if 'Dense' in env_id: + config += '-dense' + else: + config += '-sparse' + env_id = env_id.replace('Dense', '') + + # Process and smooth data. + assert success_rate.shape == epoch.shape + x = epoch + y = success_rate + if args.smooth: + x, y = smooth_reward_curve(epoch, success_rate) + assert x.shape == y.shape + + if env_id not in data: + data[env_id] = {} + if config not in data[env_id]: + data[env_id][config] = [] + data[env_id][config].append((x, y)) + +# Plot data. +for env_id in sorted(data.keys()): + print('exporting {}'.format(env_id)) + plt.clf() + + for config in sorted(data[env_id].keys()): + xs, ys = zip(*data[env_id][config]) + xs, ys = pad(xs), pad(ys) + assert xs.shape == ys.shape + + plt.plot(xs[0], np.nanmedian(ys, axis=0), label=config) + plt.fill_between(xs[0], np.nanpercentile(ys, 25, axis=0), np.nanpercentile(ys, 75, axis=0), alpha=0.25) + plt.title(env_id) + plt.xlabel('Epoch') + plt.ylabel('Median Success Rate') + plt.legend() + plt.savefig(os.path.join(args.dir, 'fig_{}.png'.format(env_id))) diff --git a/baselines/her/her.py b/baselines/her/her.py new file mode 100644 index 0000000000000000000000000000000000000000..1821fc5d762c323f6f15b33f5a8a7889bdb49b84 --- /dev/null +++ b/baselines/her/her.py @@ -0,0 +1,193 @@ +import os + +import click +import numpy as np +import json +from mpi4py import MPI + +from baselines import logger +from baselines.common import set_global_seeds, tf_util +from baselines.common.mpi_moments import mpi_moments +import baselines.her.experiment.config as config +from baselines.her.rollout import RolloutWorker + +def mpi_average(value): + if not isinstance(value, list): + value = [value] + if not any(value): + value = [0.] + return mpi_moments(np.array(value))[0] + + +def train(*, policy, rollout_worker, evaluator, + n_epochs, n_test_rollouts, n_cycles, n_batches, policy_save_interval, + save_path, demo_file, **kwargs): + rank = MPI.COMM_WORLD.Get_rank() + + if save_path: + latest_policy_path = os.path.join(save_path, 'policy_latest.pkl') + best_policy_path = os.path.join(save_path, 'policy_best.pkl') + periodic_policy_path = os.path.join(save_path, 'policy_{}.pkl') + + logger.info("Training...") + best_success_rate = -1 + + if policy.bc_loss == 1: policy.init_demo_buffer(demo_file) #initialize demo buffer if training with demonstrations + + # num_timesteps = n_epochs * n_cycles * rollout_length * number of rollout workers + for epoch in range(n_epochs): + # train + rollout_worker.clear_history() + for _ in range(n_cycles): + episode = rollout_worker.generate_rollouts() + policy.store_episode(episode) + for _ in range(n_batches): + policy.train() + policy.update_target_net() + + # test + evaluator.clear_history() + for _ in range(n_test_rollouts): + evaluator.generate_rollouts() + + # record logs + logger.record_tabular('epoch', epoch) + for key, val in evaluator.logs('test'): + logger.record_tabular(key, mpi_average(val)) + for key, val in rollout_worker.logs('train'): + logger.record_tabular(key, mpi_average(val)) + for key, val in policy.logs(): + logger.record_tabular(key, mpi_average(val)) + + if rank == 0: + logger.dump_tabular() + + # save the policy if it's better than the previous ones + success_rate = mpi_average(evaluator.current_success_rate()) + if rank == 0 and success_rate >= best_success_rate and save_path: + best_success_rate = success_rate + logger.info('New best success rate: {}. Saving policy to {} ...'.format(best_success_rate, best_policy_path)) + evaluator.save_policy(best_policy_path) + evaluator.save_policy(latest_policy_path) + if rank == 0 and policy_save_interval > 0 and epoch % policy_save_interval == 0 and save_path: + policy_path = periodic_policy_path.format(epoch) + logger.info('Saving periodic policy to {} ...'.format(policy_path)) + evaluator.save_policy(policy_path) + + # make sure that different threads have different seeds + local_uniform = np.random.uniform(size=(1,)) + root_uniform = local_uniform.copy() + MPI.COMM_WORLD.Bcast(root_uniform, root=0) + if rank != 0: + assert local_uniform[0] != root_uniform[0] + + return policy + + +def learn(*, network, env, total_timesteps, + seed=None, + eval_env=None, + replay_strategy='future', + policy_save_interval=5, + clip_return=True, + demo_file=None, + override_params=None, + load_path=None, + save_path=None, + **kwargs +): + + override_params = override_params or {} + if MPI is not None: + rank = MPI.COMM_WORLD.Get_rank() + num_cpu = MPI.COMM_WORLD.Get_size() + + # Seed everything. + rank_seed = seed + 1000000 * rank if seed is not None else None + set_global_seeds(rank_seed) + + # Prepare params. + params = config.DEFAULT_PARAMS + env_name = env.spec.id + params['env_name'] = env_name + params['replay_strategy'] = replay_strategy + if env_name in config.DEFAULT_ENV_PARAMS: + params.update(config.DEFAULT_ENV_PARAMS[env_name]) # merge env-specific parameters in + params.update(**override_params) # makes it possible to override any parameter + with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f: + json.dump(params, f) + params = config.prepare_params(params) + params['rollout_batch_size'] = env.num_envs + + if demo_file is not None: + params['bc_loss'] = 1 + params.update(kwargs) + + config.log_params(params, logger=logger) + + if num_cpu == 1: + logger.warn() + logger.warn('*** Warning ***') + logger.warn( + 'You are running HER with just a single MPI worker. This will work, but the ' + + 'experiments that we report in Plappert et al. (2018, https://arxiv.org/abs/1802.09464) ' + + 'were obtained with --num_cpu 19. This makes a significant difference and if you ' + + 'are looking to reproduce those results, be aware of this. Please also refer to ' + + 'https://github.com/openai/baselines/issues/314 for further details.') + logger.warn('****************') + logger.warn() + + dims = config.configure_dims(params) + policy = config.configure_ddpg(dims=dims, params=params, clip_return=clip_return) + if load_path is not None: + tf_util.load_variables(load_path) + + rollout_params = { + 'exploit': False, + 'use_target_net': False, + 'use_demo_states': True, + 'compute_Q': False, + 'T': params['T'], + } + + eval_params = { + 'exploit': True, + 'use_target_net': params['test_with_polyak'], + 'use_demo_states': False, + 'compute_Q': True, + 'T': params['T'], + } + + for name in ['T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps']: + rollout_params[name] = params[name] + eval_params[name] = params[name] + + eval_env = eval_env or env + + rollout_worker = RolloutWorker(env, policy, dims, logger, monitor=True, **rollout_params) + evaluator = RolloutWorker(eval_env, policy, dims, logger, **eval_params) + + n_cycles = params['n_cycles'] + n_epochs = total_timesteps // n_cycles // rollout_worker.T // rollout_worker.rollout_batch_size + + return train( + save_path=save_path, policy=policy, rollout_worker=rollout_worker, + evaluator=evaluator, n_epochs=n_epochs, n_test_rollouts=params['n_test_rollouts'], + n_cycles=params['n_cycles'], n_batches=params['n_batches'], + policy_save_interval=policy_save_interval, demo_file=demo_file) + + +@click.command() +@click.option('--env', type=str, default='FetchReach-v1', help='the name of the OpenAI Gym environment that you want to train on') +@click.option('--total_timesteps', type=int, default=int(5e5), help='the number of timesteps to run') +@click.option('--seed', type=int, default=0, help='the random seed used to seed both the environment and the training code') +@click.option('--policy_save_interval', type=int, default=5, help='the interval with which policy pickles are saved. If set to 0, only the best and latest policy will be pickled.') +@click.option('--replay_strategy', type=click.Choice(['future', 'none']), default='future', help='the HER replay strategy to be used. "future" uses HER, "none" disables HER.') +@click.option('--clip_return', type=int, default=1, help='whether or not returns should be clipped') +@click.option('--demo_file', type=str, default = 'PATH/TO/DEMO/DATA/FILE.npz', help='demo data file path') +def main(**kwargs): + learn(**kwargs) + + +if __name__ == '__main__': + main() diff --git a/baselines/her/her_sampler.py b/baselines/her/her_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..76f3c346ae34f93150926ba70d1a4987be484ddd --- /dev/null +++ b/baselines/her/her_sampler.py @@ -0,0 +1,63 @@ +import numpy as np + + +def make_sample_her_transitions(replay_strategy, replay_k, reward_fun): + """Creates a sample function that can be used for HER experience replay. + + Args: + replay_strategy (in ['future', 'none']): the HER replay strategy; if set to 'none', + regular DDPG experience replay is used + replay_k (int): the ratio between HER replays and regular replays (e.g. k = 4 -> 4 times + as many HER replays as regular replays are used) + reward_fun (function): function to re-compute the reward with substituted goals + """ + if replay_strategy == 'future': + future_p = 1 - (1. / (1 + replay_k)) + else: # 'replay_strategy' == 'none' + future_p = 0 + + def _sample_her_transitions(episode_batch, batch_size_in_transitions): + """episode_batch is {key: array(buffer_size x T x dim_key)} + """ + T = episode_batch['u'].shape[1] + rollout_batch_size = episode_batch['u'].shape[0] + batch_size = batch_size_in_transitions + + # Select which episodes and time steps to use. + episode_idxs = np.random.randint(0, rollout_batch_size, batch_size) + t_samples = np.random.randint(T, size=batch_size) + transitions = {key: episode_batch[key][episode_idxs, t_samples].copy() + for key in episode_batch.keys()} + + # Select future time indexes proportional with probability future_p. These + # will be used for HER replay by substituting in future goals. + her_indexes = np.where(np.random.uniform(size=batch_size) < future_p) + future_offset = np.random.uniform(size=batch_size) * (T - t_samples) + future_offset = future_offset.astype(int) + future_t = (t_samples + 1 + future_offset)[her_indexes] + + # Replace goal with achieved goal but only for the previously-selected + # HER transitions (as defined by her_indexes). For the other transitions, + # keep the original goal. + future_ag = episode_batch['ag'][episode_idxs[her_indexes], future_t] + transitions['g'][her_indexes] = future_ag + + # Reconstruct info dictionary for reward computation. + info = {} + for key, value in transitions.items(): + if key.startswith('info_'): + info[key.replace('info_', '')] = value + + # Re-compute reward since we may have substituted the goal. + reward_params = {k: transitions[k] for k in ['ag_2', 'g']} + reward_params['info'] = info + transitions['r'] = reward_fun(**reward_params) + + transitions = {k: transitions[k].reshape(batch_size, *transitions[k].shape[1:]) + for k in transitions.keys()} + + assert(transitions['u'].shape[0] == batch_size_in_transitions) + + return transitions + + return _sample_her_transitions diff --git a/baselines/her/normalizer.py b/baselines/her/normalizer.py new file mode 100644 index 0000000000000000000000000000000000000000..40be70c30c765a1dd6f65a43f612ca06ba93a1f5 --- /dev/null +++ b/baselines/her/normalizer.py @@ -0,0 +1,140 @@ +import threading + +import numpy as np +from mpi4py import MPI +import tensorflow as tf + +from baselines.her.util import reshape_for_broadcasting + + +class Normalizer: + def __init__(self, size, eps=1e-2, default_clip_range=np.inf, sess=None): + """A normalizer that ensures that observations are approximately distributed according to + a standard Normal distribution (i.e. have mean zero and variance one). + + Args: + size (int): the size of the observation to be normalized + eps (float): a small constant that avoids underflows + default_clip_range (float): normalized observations are clipped to be in + [-default_clip_range, default_clip_range] + sess (object): the TensorFlow session to be used + """ + self.size = size + self.eps = eps + self.default_clip_range = default_clip_range + self.sess = sess if sess is not None else tf.compat.v1.get_default_session() + + self.local_sum = np.zeros(self.size, np.float32) + self.local_sumsq = np.zeros(self.size, np.float32) + self.local_count = np.zeros(1, np.float32) + + self.sum_tf = tf.compat.v1.get_variable( + initializer=tf.compat.v1.zeros_initializer(), shape=self.local_sum.shape, name='sum', + trainable=False, dtype=tf.float32) + self.sumsq_tf = tf.compat.v1.get_variable( + initializer=tf.compat.v1.zeros_initializer(), shape=self.local_sumsq.shape, name='sumsq', + trainable=False, dtype=tf.float32) + self.count_tf = tf.compat.v1.get_variable( + initializer=tf.compat.v1.ones_initializer(), shape=self.local_count.shape, name='count', + trainable=False, dtype=tf.float32) + self.mean = tf.compat.v1.get_variable( + initializer=tf.compat.v1.zeros_initializer(), shape=(self.size,), name='mean', + trainable=False, dtype=tf.float32) + self.std = tf.compat.v1.get_variable( + initializer=tf.compat.v1.ones_initializer(), shape=(self.size,), name='std', + trainable=False, dtype=tf.float32) + self.count_pl = tf.compat.v1.placeholder(name='count_pl', shape=(1,), dtype=tf.float32) + self.sum_pl = tf.compat.v1.placeholder(name='sum_pl', shape=(self.size,), dtype=tf.float32) + self.sumsq_pl = tf.compat.v1.placeholder(name='sumsq_pl', shape=(self.size,), dtype=tf.float32) + + self.update_op = tf.group( + self.count_tf.assign_add(self.count_pl), + self.sum_tf.assign_add(self.sum_pl), + self.sumsq_tf.assign_add(self.sumsq_pl) + ) + self.recompute_op = tf.group( + tf.compat.v1.assign(self.mean, self.sum_tf / self.count_tf), + tf.compat.v1.assign(self.std, tf.sqrt(tf.maximum( + tf.square(self.eps), + self.sumsq_tf / self.count_tf - tf.square(self.sum_tf / self.count_tf) + ))), + ) + self.lock = threading.Lock() + + def update(self, v): + v = v.reshape(-1, self.size) + + with self.lock: + self.local_sum += v.sum(axis=0) + self.local_sumsq += (np.square(v)).sum(axis=0) + self.local_count[0] += v.shape[0] + + def normalize(self, v, clip_range=None): + if clip_range is None: + clip_range = self.default_clip_range + mean = reshape_for_broadcasting(self.mean, v) + std = reshape_for_broadcasting(self.std, v) + return tf.clip_by_value((v - mean) / std, -clip_range, clip_range) + + def denormalize(self, v): + mean = reshape_for_broadcasting(self.mean, v) + std = reshape_for_broadcasting(self.std, v) + return mean + v * std + + def _mpi_average(self, x): + buf = np.zeros_like(x) + MPI.COMM_WORLD.Allreduce(x, buf, op=MPI.SUM) + buf /= MPI.COMM_WORLD.Get_size() + return buf + + def synchronize(self, local_sum, local_sumsq, local_count, root=None): + local_sum[...] = self._mpi_average(local_sum) + local_sumsq[...] = self._mpi_average(local_sumsq) + local_count[...] = self._mpi_average(local_count) + return local_sum, local_sumsq, local_count + + def recompute_stats(self): + with self.lock: + # Copy over results. + local_count = self.local_count.copy() + local_sum = self.local_sum.copy() + local_sumsq = self.local_sumsq.copy() + + # Reset. + self.local_count[...] = 0 + self.local_sum[...] = 0 + self.local_sumsq[...] = 0 + + # We perform the synchronization outside of the lock to keep the critical section as short + # as possible. + synced_sum, synced_sumsq, synced_count = self.synchronize( + local_sum=local_sum, local_sumsq=local_sumsq, local_count=local_count) + + self.sess.run(self.update_op, feed_dict={ + self.count_pl: synced_count, + self.sum_pl: synced_sum, + self.sumsq_pl: synced_sumsq, + }) + self.sess.run(self.recompute_op) + + +class IdentityNormalizer: + def __init__(self, size, std=1.): + self.size = size + self.mean = tf.zeros(self.size, tf.float32) + self.std = std * tf.ones(self.size, tf.float32) + + def update(self, x): + pass + + def normalize(self, x, clip_range=None): + return x / self.std + + def denormalize(self, x): + return self.std * x + + def synchronize(self): + pass + + def recompute_stats(self): + pass diff --git a/baselines/her/replay_buffer.py b/baselines/her/replay_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..b0005523fd63a8f85acc50ab8a30b0c2b8253d1f --- /dev/null +++ b/baselines/her/replay_buffer.py @@ -0,0 +1,108 @@ +import threading + +import numpy as np + + +class ReplayBuffer: + def __init__(self, buffer_shapes, size_in_transitions, T, sample_transitions): + """Creates a replay buffer. + + Args: + buffer_shapes (dict of ints): the shape for all buffers that are used in the replay + buffer + size_in_transitions (int): the size of the buffer, measured in transitions + T (int): the time horizon for episodes + sample_transitions (function): a function that samples from the replay buffer + """ + self.buffer_shapes = buffer_shapes + self.size = size_in_transitions // T + self.T = T + self.sample_transitions = sample_transitions + + # self.buffers is {key: array(size_in_episodes x T or T+1 x dim_key)} + self.buffers = {key: np.empty([self.size, *shape]) + for key, shape in buffer_shapes.items()} + + # memory management + self.current_size = 0 + self.n_transitions_stored = 0 + + self.lock = threading.Lock() + + @property + def full(self): + with self.lock: + return self.current_size == self.size + + def sample(self, batch_size): + """Returns a dict {key: array(batch_size x shapes[key])} + """ + buffers = {} + + with self.lock: + assert self.current_size > 0 + for key in self.buffers.keys(): + buffers[key] = self.buffers[key][:self.current_size] + + buffers['o_2'] = buffers['o'][:, 1:, :] + buffers['ag_2'] = buffers['ag'][:, 1:, :] + + transitions = self.sample_transitions(buffers, batch_size) + + for key in (['r', 'o_2', 'ag_2'] + list(self.buffers.keys())): + assert key in transitions, "key %s missing from transitions" % key + + return transitions + + def store_episode(self, episode_batch): + """episode_batch: array(batch_size x (T or T+1) x dim_key) + """ + batch_sizes = [len(episode_batch[key]) for key in episode_batch.keys()] + assert np.all(np.array(batch_sizes) == batch_sizes[0]) + batch_size = batch_sizes[0] + + with self.lock: + idxs = self._get_storage_idx(batch_size) + + # load inputs into buffers + for key in self.buffers.keys(): + self.buffers[key][idxs] = episode_batch[key] + + self.n_transitions_stored += batch_size * self.T + + def get_current_episode_size(self): + with self.lock: + return self.current_size + + def get_current_size(self): + with self.lock: + return self.current_size * self.T + + def get_transitions_stored(self): + with self.lock: + return self.n_transitions_stored + + def clear_buffer(self): + with self.lock: + self.current_size = 0 + + def _get_storage_idx(self, inc=None): + inc = inc or 1 # size increment + assert inc <= self.size, "Batch committed to replay is too large!" + # go consecutively until you hit the end, and then go randomly. + if self.current_size+inc <= self.size: + idx = np.arange(self.current_size, self.current_size+inc) + elif self.current_size < self.size: + overflow = inc - (self.size - self.current_size) + idx_a = np.arange(self.current_size, self.size) + idx_b = np.random.randint(0, self.current_size, overflow) + idx = np.concatenate([idx_a, idx_b]) + else: + idx = np.random.randint(0, self.size, inc) + + # update replay size + self.current_size = min(self.size, self.current_size+inc) + + if inc == 1: + idx = idx[0] + return idx diff --git a/baselines/her/rollout.py b/baselines/her/rollout.py new file mode 100644 index 0000000000000000000000000000000000000000..e68eb65840f566055bb31dc0c60b2198633eedbd --- /dev/null +++ b/baselines/her/rollout.py @@ -0,0 +1,169 @@ +from collections import deque + +import numpy as np +import pickle + +from baselines.her.util import convert_episode_to_batch_major, store_args + + +class RolloutWorker: + + @store_args + def __init__(self, venv, policy, dims, logger, T, rollout_batch_size=1, + exploit=False, use_target_net=False, compute_Q=False, noise_eps=0, + random_eps=0, history_len=100, render=False, monitor=False, **kwargs): + """Rollout worker generates experience by interacting with one or many environments. + + Args: + venv: vectorized gym environments. + policy (object): the policy that is used to act + dims (dict of ints): the dimensions for observations (o), goals (g), and actions (u) + logger (object): the logger that is used by the rollout worker + rollout_batch_size (int): the number of parallel rollouts that should be used + exploit (boolean): whether or not to exploit, i.e. to act optimally according to the + current policy without any exploration + use_target_net (boolean): whether or not to use the target net for rollouts + compute_Q (boolean): whether or not to compute the Q values alongside the actions + noise_eps (float): scale of the additive Gaussian noise + random_eps (float): probability of selecting a completely random action + history_len (int): length of history for statistics smoothing + render (boolean): whether or not to render the rollouts + """ + + assert self.T > 0 + + self.info_keys = [key.replace('info_', '') for key in dims.keys() if key.startswith('info_')] + + self.success_history = deque(maxlen=history_len) + self.Q_history = deque(maxlen=history_len) + + self.n_episodes = 0 + self.reset_all_rollouts() + self.clear_history() + + def reset_all_rollouts(self): + self.obs_dict = self.venv.reset() + self.initial_o = self.obs_dict['observation'] + self.initial_ag = self.obs_dict['achieved_goal'] + self.g = self.obs_dict['desired_goal'] + + def generate_rollouts(self): + """Performs `rollout_batch_size` rollouts in parallel for time horizon `T` with the current + policy acting on it accordingly. + """ + self.reset_all_rollouts() + + # compute observations + o = np.empty((self.rollout_batch_size, self.dims['o']), np.float32) # observations + ag = np.empty((self.rollout_batch_size, self.dims['g']), np.float32) # achieved goals + o[:] = self.initial_o + ag[:] = self.initial_ag + + # generate episodes + obs, achieved_goals, acts, goals, successes = [], [], [], [], [] + dones = [] + info_values = [np.empty((self.T - 1, self.rollout_batch_size, self.dims['info_' + key]), np.float32) for key in self.info_keys] + Qs = [] + for t in range(self.T): + policy_output = self.policy.get_actions( + o, ag, self.g, + compute_Q=self.compute_Q, + noise_eps=self.noise_eps if not self.exploit else 0., + random_eps=self.random_eps if not self.exploit else 0., + use_target_net=self.use_target_net) + + if self.compute_Q: + u, Q = policy_output + Qs.append(Q) + else: + u = policy_output + + if u.ndim == 1: + # The non-batched case should still have a reasonable shape. + u = u.reshape(1, -1) + + o_new = np.empty((self.rollout_batch_size, self.dims['o'])) + ag_new = np.empty((self.rollout_batch_size, self.dims['g'])) + success = np.zeros(self.rollout_batch_size) + # compute new states and observations + obs_dict_new, _, done, info = self.venv.step(u) + o_new = obs_dict_new['observation'] + ag_new = obs_dict_new['achieved_goal'] + success = np.array([i.get('is_success', 0.0) for i in info]) + + if any(done): + # here we assume all environments are done is ~same number of steps, so we terminate rollouts whenever any of the envs returns done + # trick with using vecenvs is not to add the obs from the environments that are "done", because those are already observations + # after a reset + break + + for i, info_dict in enumerate(info): + for idx, key in enumerate(self.info_keys): + info_values[idx][t, i] = info[i][key] + + if np.isnan(o_new).any(): + self.logger.warn('NaN caught during rollout generation. Trying again...') + self.reset_all_rollouts() + return self.generate_rollouts() + + dones.append(done) + obs.append(o.copy()) + achieved_goals.append(ag.copy()) + successes.append(success.copy()) + acts.append(u.copy()) + goals.append(self.g.copy()) + o[...] = o_new + ag[...] = ag_new + obs.append(o.copy()) + achieved_goals.append(ag.copy()) + + episode = dict(o=obs, + u=acts, + g=goals, + ag=achieved_goals) + for key, value in zip(self.info_keys, info_values): + episode['info_{}'.format(key)] = value + + # stats + successful = np.array(successes)[-1, :] + assert successful.shape == (self.rollout_batch_size,) + success_rate = np.mean(successful) + self.success_history.append(success_rate) + if self.compute_Q: + self.Q_history.append(np.mean(Qs)) + self.n_episodes += self.rollout_batch_size + + return convert_episode_to_batch_major(episode) + + def clear_history(self): + """Clears all histories that are used for statistics + """ + self.success_history.clear() + self.Q_history.clear() + + def current_success_rate(self): + return np.mean(self.success_history) + + def current_mean_Q(self): + return np.mean(self.Q_history) + + def save_policy(self, path): + """Pickles the current policy for later inspection. + """ + with open(path, 'wb') as f: + pickle.dump(self.policy, f) + + def logs(self, prefix='worker'): + """Generates a dictionary that contains all collected statistics. + """ + logs = [] + logs += [('success_rate', np.mean(self.success_history))] + if self.compute_Q: + logs += [('mean_Q', np.mean(self.Q_history))] + logs += [('episode', self.n_episodes)] + + if prefix != '' and not prefix.endswith('/'): + return [(prefix + '/' + key, val) for key, val in logs] + else: + return logs + diff --git a/baselines/her/util.py b/baselines/her/util.py new file mode 100644 index 0000000000000000000000000000000000000000..b57f77a77ef117b9b0b76c1a384baa06037c1d79 --- /dev/null +++ b/baselines/her/util.py @@ -0,0 +1,140 @@ +import os +import subprocess +import sys +import importlib +import inspect +import functools + +import tensorflow as tf +import numpy as np + +from baselines.common import tf_util as U + + +def store_args(method): + """Stores provided method args as instance attributes. + """ + argspec = inspect.getfullargspec(method) + defaults = {} + if argspec.defaults is not None: + defaults = dict( + zip(argspec.args[-len(argspec.defaults):], argspec.defaults)) + if argspec.kwonlydefaults is not None: + defaults.update(argspec.kwonlydefaults) + arg_names = argspec.args[1:] + + @functools.wraps(method) + def wrapper(*positional_args, **keyword_args): + self = positional_args[0] + # Get default arg values + args = defaults.copy() + # Add provided arg values + for name, value in zip(arg_names, positional_args[1:]): + args[name] = value + args.update(keyword_args) + self.__dict__.update(args) + return method(*positional_args, **keyword_args) + + return wrapper + + +def import_function(spec): + """Import a function identified by a string like "pkg.module:fn_name". + """ + mod_name, fn_name = spec.split(':') + module = importlib.import_module(mod_name) + fn = getattr(module, fn_name) + return fn + + +def flatten_grads(var_list, grads): + """Flattens a variables and their gradients. + """ + return tf.concat([tf.reshape(grad, [U.numel(v)]) + for (v, grad) in zip(var_list, grads)], 0) + + +def nn(input, layers_sizes, reuse=None, flatten=False, name=""): + """Creates a simple neural network + """ + for i, size in enumerate(layers_sizes): + activation = tf.nn.relu if i < len(layers_sizes) - 1 else None + input = tf.compat.v1.layers.dense(inputs=input, + units=size, + kernel_initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), + reuse=reuse, + name=name + '_' + str(i)) + if activation: + input = activation(input) + if flatten: + assert layers_sizes[-1] == 1 + input = tf.reshape(input, [-1]) + return input + + +def install_mpi_excepthook(): + import sys + from mpi4py import MPI + old_hook = sys.excepthook + + def new_hook(a, b, c): + old_hook(a, b, c) + sys.stdout.flush() + sys.stderr.flush() + MPI.COMM_WORLD.Abort() + sys.excepthook = new_hook + + +def mpi_fork(n, extra_mpi_args=[]): + """Re-launches the current script with workers + Returns "parent" for original parent, "child" for MPI children + """ + if n <= 1: + return "child" + if os.getenv("IN_MPI") is None: + env = os.environ.copy() + env.update( + MKL_NUM_THREADS="1", + OMP_NUM_THREADS="1", + IN_MPI="1" + ) + # "-bind-to core" is crucial for good performance + args = ["mpirun", "-np", str(n)] + \ + extra_mpi_args + \ + [sys.executable] + + args += sys.argv + subprocess.check_call(args, env=env) + return "parent" + else: + install_mpi_excepthook() + return "child" + + +def convert_episode_to_batch_major(episode): + """Converts an episode to have the batch dimension in the major (first) + dimension. + """ + episode_batch = {} + for key in episode.keys(): + val = np.array(episode[key]).copy() + # make inputs batch-major instead of time-major + episode_batch[key] = val.swapaxes(0, 1) + + return episode_batch + + +def transitions_in_episode_batch(episode_batch): + """Number of transitions in a given episode batch. + """ + shape = episode_batch['u'].shape + return shape[0] * shape[1] + + +def reshape_for_broadcasting(source, target): + """Reshapes a tensor (source) to have the correct shape and dtype of the target + before broadcasting it with MPI. + """ + dim = len(target.get_shape()) + shape = ([1] * (dim - 1)) + [-1] + return tf.reshape(tf.cast(source, target.dtype), shape) diff --git a/baselines/logger.py b/baselines/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..1f14e15cfd1d33321d9c422b488fada0b3659de0 --- /dev/null +++ b/baselines/logger.py @@ -0,0 +1,502 @@ +import os +import sys +import shutil +import os.path as osp +import json +import time +import datetime +import tempfile +from collections import defaultdict +from contextlib import contextmanager + +DEBUG = 10 +INFO = 20 +WARN = 30 +ERROR = 40 + +DISABLED = 50 + +class KVWriter(object): + def writekvs(self, kvs): + raise NotImplementedError + +class SeqWriter(object): + def writeseq(self, seq): + raise NotImplementedError + +class HumanOutputFormat(KVWriter, SeqWriter): + def __init__(self, filename_or_file): + if isinstance(filename_or_file, str): + self.file = open(filename_or_file, 'wt') + self.own_file = True + else: + assert hasattr(filename_or_file, 'read'), 'expected file or str, got %s'%filename_or_file + self.file = filename_or_file + self.own_file = False + + def writekvs(self, kvs): + # Create strings for printing + key2str = {} + for (key, val) in sorted(kvs.items()): + if hasattr(val, '__float__'): + valstr = '%-8.3g' % val + else: + valstr = str(val) + key2str[self._truncate(key)] = self._truncate(valstr) + + # Find max widths + if len(key2str) == 0: + print('WARNING: tried to write empty key-value dict') + return + else: + keywidth = max(map(len, key2str.keys())) + valwidth = max(map(len, key2str.values())) + + # Write out the data + dashes = '-' * (keywidth + valwidth + 7) + lines = [dashes] + for (key, val) in sorted(key2str.items(), key=lambda kv: kv[0].lower()): + lines.append('| %s%s | %s%s |' % ( + key, + ' ' * (keywidth - len(key)), + val, + ' ' * (valwidth - len(val)), + )) + lines.append(dashes) + self.file.write('\n'.join(lines) + '\n') + + # Flush the output to the file + self.file.flush() + + def _truncate(self, s): + maxlen = 30 + return s[:maxlen-3] + '...' if len(s) > maxlen else s + + def writeseq(self, seq): + seq = list(seq) + for (i, elem) in enumerate(seq): + self.file.write(elem) + if i < len(seq) - 1: # add space unless this is the last one + self.file.write(' ') + self.file.write('\n') + self.file.flush() + + def close(self): + if self.own_file: + self.file.close() + +class JSONOutputFormat(KVWriter): + def __init__(self, filename): + self.file = open(filename, 'wt') + + def writekvs(self, kvs): + for k, v in sorted(kvs.items()): + if hasattr(v, 'dtype'): + kvs[k] = float(v) + self.file.write(json.dumps(kvs) + '\n') + self.file.flush() + + def close(self): + self.file.close() + +class CSVOutputFormat(KVWriter): + def __init__(self, filename): + self.file = open(filename, 'w+t') + self.keys = [] + self.sep = ',' + + def writekvs(self, kvs): + # Add our current row to the history + extra_keys = list(kvs.keys() - self.keys) + extra_keys.sort() + if extra_keys: + self.keys.extend(extra_keys) + self.file.seek(0) + lines = self.file.readlines() + self.file.seek(0) + for (i, k) in enumerate(self.keys): + if i > 0: + self.file.write(',') + self.file.write(k) + self.file.write('\n') + for line in lines[1:]: + self.file.write(line[:-1]) + self.file.write(self.sep * len(extra_keys)) + self.file.write('\n') + for (i, k) in enumerate(self.keys): + if i > 0: + self.file.write(',') + v = kvs.get(k) + if v is not None: + self.file.write(str(v)) + self.file.write('\n') + self.file.flush() + + def close(self): + self.file.close() + + +class TensorBoardOutputFormat(KVWriter): + """ + Dumps key/value pairs into TensorBoard's numeric format. + """ + def __init__(self, dir): + os.makedirs(dir, exist_ok=True) + self.dir = dir + self.step = 1 + prefix = 'events' + path = osp.join(osp.abspath(dir), prefix) + import tensorflow as tf + from tensorflow.python import pywrap_tensorflow + from tensorflow.core.util import event_pb2 + from tensorflow.python.util import compat + self.tf = tf + self.event_pb2 = event_pb2 + self.pywrap_tensorflow = pywrap_tensorflow + self.writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(path)) + + def writekvs(self, kvs): + def summary_val(k, v): + kwargs = {'tag': k, 'simple_value': float(v)} + return self.tf.Summary.Value(**kwargs) + summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()]) + event = self.event_pb2.Event(wall_time=time.time(), summary=summary) + event.step = self.step # is there any reason why you'd want to specify the step? + self.writer.WriteEvent(event) + self.writer.Flush() + self.step += 1 + + def close(self): + if self.writer: + self.writer.Close() + self.writer = None + +def make_output_format(format, ev_dir, log_suffix=''): + os.makedirs(ev_dir, exist_ok=True) + if format == 'stdout': + return HumanOutputFormat(sys.stdout) + elif format == 'log': + return HumanOutputFormat(osp.join(ev_dir, 'log%s.txt' % log_suffix)) + elif format == 'json': + return JSONOutputFormat(osp.join(ev_dir, 'progress%s.json' % log_suffix)) + elif format == 'csv': + return CSVOutputFormat(osp.join(ev_dir, 'progress%s.csv' % log_suffix)) + elif format == 'tensorboard': + return TensorBoardOutputFormat(osp.join(ev_dir, 'tb%s' % log_suffix)) + else: + raise ValueError('Unknown format specified: %s' % (format,)) + +# ================================================================ +# API +# ================================================================ + +def logkv(key, val): + """ + Log a value of some diagnostic + Call this once for each diagnostic quantity, each iteration + If called many times, last value will be used. + """ + get_current().logkv(key, val) + +def logkv_mean(key, val): + """ + The same as logkv(), but if called many times, values averaged. + """ + get_current().logkv_mean(key, val) + +def logkvs(d): + """ + Log a dictionary of key-value pairs + """ + for (k, v) in d.items(): + logkv(k, v) + +def dumpkvs(): + """ + Write all of the diagnostics from the current iteration + """ + return get_current().dumpkvs() + +def getkvs(): + return get_current().name2val + + +def log(*args, level=INFO): + """ + Write the sequence of args, with no separators, to the console and output files (if you've configured an output file). + """ + get_current().log(*args, level=level) + +def debug(*args): + log(*args, level=DEBUG) + +def info(*args): + log(*args, level=INFO) + +def warn(*args): + log(*args, level=WARN) + +def error(*args): + log(*args, level=ERROR) + + +def set_level(level): + """ + Set logging threshold on current logger. + """ + get_current().set_level(level) + +def set_comm(comm): + get_current().set_comm(comm) + +def get_dir(): + """ + Get directory that log files are being written to. + will be None if there is no output directory (i.e., if you didn't call start) + """ + return get_current().get_dir() + +record_tabular = logkv +dump_tabular = dumpkvs + +@contextmanager +def profile_kv(scopename): + logkey = 'wait_' + scopename + tstart = time.time() + try: + yield + finally: + get_current().name2val[logkey] += time.time() - tstart + +def profile(n): + """ + Usage: + @profile("my_func") + def my_func(): code + """ + def decorator_with_name(func): + def func_wrapper(*args, **kwargs): + with profile_kv(n): + return func(*args, **kwargs) + return func_wrapper + return decorator_with_name + + +# ================================================================ +# Backend +# ================================================================ + +def get_current(): + if Logger.CURRENT is None: + _configure_default_logger() + + return Logger.CURRENT + + +class Logger(object): + DEFAULT = None # A logger with no output files. (See right below class definition) + # So that you can still log to the terminal without setting up any output files + CURRENT = None # Current logger being used by the free functions above + + def __init__(self, dir, output_formats, comm=None): + self.name2val = defaultdict(float) # values this iteration + self.name2cnt = defaultdict(int) + self.level = INFO + self.dir = dir + self.output_formats = output_formats + self.comm = comm + + # Logging API, forwarded + # ---------------------------------------- + def logkv(self, key, val): + self.name2val[key] = val + + def logkv_mean(self, key, val): + oldval, cnt = self.name2val[key], self.name2cnt[key] + self.name2val[key] = oldval*cnt/(cnt+1) + val/(cnt+1) + self.name2cnt[key] = cnt + 1 + + def dumpkvs(self): + if self.comm is None: + d = self.name2val + else: + from baselines.common import mpi_util + d = mpi_util.mpi_weighted_mean(self.comm, + {name : (val, self.name2cnt.get(name, 1)) + for (name, val) in self.name2val.items()}) + if self.comm.rank != 0: + d['dummy'] = 1 # so we don't get a warning about empty dict + out = d.copy() # Return the dict for unit testing purposes + for fmt in self.output_formats: + if isinstance(fmt, KVWriter): + fmt.writekvs(d) + self.name2val.clear() + self.name2cnt.clear() + return out + + def log(self, *args, level=INFO): + if self.level <= level: + self._do_log(args) + + # Configuration + # ---------------------------------------- + def set_level(self, level): + self.level = level + + def set_comm(self, comm): + self.comm = comm + + def get_dir(self): + return self.dir + + def close(self): + for fmt in self.output_formats: + fmt.close() + + # Misc + # ---------------------------------------- + def _do_log(self, args): + for fmt in self.output_formats: + if isinstance(fmt, SeqWriter): + fmt.writeseq(map(str, args)) + +def get_rank_without_mpi_import(): + # check environment variables here instead of importing mpi4py + # to avoid calling MPI_Init() when this module is imported + for varname in ['PMI_RANK', 'OMPI_COMM_WORLD_RANK']: + if varname in os.environ: + return int(os.environ[varname]) + return 0 + + +def configure(dir=None, format_strs=None, comm=None, log_suffix=''): + """ + If comm is provided, average all numerical stats across that comm + """ + if dir is None: + dir = os.getenv('OPENAI_LOGDIR') + if dir is None: + dir = osp.join(tempfile.gettempdir(), + datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f")) + assert isinstance(dir, str) + dir = os.path.expanduser(dir) + os.makedirs(os.path.expanduser(dir), exist_ok=True) + + rank = get_rank_without_mpi_import() + if rank > 0: + log_suffix = log_suffix + "-rank%03i" % rank + + if format_strs is None: + if rank == 0: + format_strs = os.getenv('OPENAI_LOG_FORMAT', 'stdout,log,csv').split(',') + else: + format_strs = os.getenv('OPENAI_LOG_FORMAT_MPI', 'log').split(',') + format_strs = filter(None, format_strs) + output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs] + + Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm) + if output_formats: + log('Logging to %s'%dir) + +def _configure_default_logger(): + configure() + Logger.DEFAULT = Logger.CURRENT + +def reset(): + if Logger.CURRENT is not Logger.DEFAULT: + Logger.CURRENT.close() + Logger.CURRENT = Logger.DEFAULT + log('Reset logger') + +@contextmanager +def scoped_configure(dir=None, format_strs=None, comm=None): + prevlogger = Logger.CURRENT + configure(dir=dir, format_strs=format_strs, comm=comm) + try: + yield + finally: + Logger.CURRENT.close() + Logger.CURRENT = prevlogger + +# ================================================================ + +def _demo(): + info("hi") + debug("shouldn't appear") + set_level(DEBUG) + debug("should appear") + dir = "/tmp/testlogging" + if os.path.exists(dir): + shutil.rmtree(dir) + configure(dir=dir) + logkv("a", 3) + logkv("b", 2.5) + dumpkvs() + logkv("b", -2.5) + logkv("a", 5.5) + dumpkvs() + info("^^^ should see a = 5.5") + logkv_mean("b", -22.5) + logkv_mean("b", -44.4) + logkv("a", 5.5) + dumpkvs() + info("^^^ should see b = -33.3") + + logkv("b", -2.5) + dumpkvs() + + logkv("a", "longasslongasslongasslongasslongasslongassvalue") + dumpkvs() + + +# ================================================================ +# Readers +# ================================================================ + +def read_json(fname): + import pandas + ds = [] + with open(fname, 'rt') as fh: + for line in fh: + ds.append(json.loads(line)) + return pandas.DataFrame(ds) + +def read_csv(fname): + import pandas + return pandas.read_csv(fname, index_col=None, comment='#') + +def read_tb(path): + """ + path : a tensorboard file OR a directory, where we will find all TB files + of the form events.* + """ + import pandas + import numpy as np + from glob import glob + import tensorflow as tf + if osp.isdir(path): + fnames = glob(osp.join(path, "events.*")) + elif osp.basename(path).startswith("events."): + fnames = [path] + else: + raise NotImplementedError("Expected tensorboard file or directory containing them. Got %s"%path) + tag2pairs = defaultdict(list) + maxstep = 0 + for fname in fnames: + for summary in tf.compat.v1.train.summary_iterator(fname): + if summary.step > 0: + for v in summary.summary.value: + pair = (summary.step, v.simple_value) + tag2pairs[v.tag].append(pair) + maxstep = max(summary.step, maxstep) + data = np.empty((maxstep, len(tag2pairs))) + data[:] = np.nan + tags = sorted(tag2pairs.keys()) + for (colidx,tag) in enumerate(tags): + pairs = tag2pairs[tag] + for (step, value) in pairs: + data[step-1, colidx] = value + return pandas.DataFrame(data, columns=tags) + +if __name__ == "__main__": + _demo() diff --git a/baselines/ppo1/README.md b/baselines/ppo1/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1faf5adf1a8a7d046516a513453767bc32b8a3fe --- /dev/null +++ b/baselines/ppo1/README.md @@ -0,0 +1,9 @@ +# PPOSGD + +- Original paper: https://arxiv.org/abs/1707.06347 +- Baselines blog post: https://blog.openai.com/openai-baselines-ppo/ +- `mpirun -np 8 python -m baselines.ppo1.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options. +- `python -m baselines.ppo1.run_mujoco` runs the algorithm for 1M frames on a Mujoco environment. + +- Train mujoco 3d humanoid (with optimal-ish hyperparameters): `mpirun -np 16 python -m baselines.ppo1.run_humanoid --model-path=/path/to/model` +- Render the 3d humanoid: `python -m baselines.ppo1.run_humanoid --play --model-path=/path/to/model` diff --git a/baselines/ppo1/__init__.py b/baselines/ppo1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/ppo1/cnn_policy.py b/baselines/ppo1/cnn_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..84cb02ed89066e3d66b3a23f14aa54840d4202cf --- /dev/null +++ b/baselines/ppo1/cnn_policy.py @@ -0,0 +1,56 @@ +import baselines.common.tf_util as U +import tensorflow as tf +import gym +from baselines.common.distributions import make_pdtype + +class CnnPolicy(object): + recurrent = False + def __init__(self, name, ob_space, ac_space, kind='large'): + with tf.compat.v1.variable_scope(name): + self._init(ob_space, ac_space, kind) + self.scope = tf.compat.v1.get_variable_scope().name + + def _init(self, ob_space, ac_space, kind): + assert isinstance(ob_space, gym.spaces.Box) + + self.pdtype = pdtype = make_pdtype(ac_space) + sequence_length = None + + ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape)) + + x = ob / 255.0 + if kind == 'small': # from A3C paper + x = tf.nn.relu(U.conv2d(x, 16, "l1", [8, 8], [4, 4], pad="VALID")) + x = tf.nn.relu(U.conv2d(x, 32, "l2", [4, 4], [2, 2], pad="VALID")) + x = U.flattenallbut0(x) + x = tf.nn.relu(tf.compat.v1.layers.dense(x, 256, name='lin', kernel_initializer=U.normc_initializer(1.0))) + elif kind == 'large': # Nature DQN + x = tf.nn.relu(U.conv2d(x, 32, "l1", [8, 8], [4, 4], pad="VALID")) + x = tf.nn.relu(U.conv2d(x, 64, "l2", [4, 4], [2, 2], pad="VALID")) + x = tf.nn.relu(U.conv2d(x, 64, "l3", [3, 3], [1, 1], pad="VALID")) + x = U.flattenallbut0(x) + x = tf.nn.relu(tf.compat.v1.layers.dense(x, 512, name='lin', kernel_initializer=U.normc_initializer(1.0))) + else: + raise NotImplementedError + + logits = tf.compat.v1.layers.dense(x, pdtype.param_shape()[0], name='logits', kernel_initializer=U.normc_initializer(0.01)) + self.pd = pdtype.pdfromflat(logits) + self.vpred = tf.compat.v1.layers.dense(x, 1, name='value', kernel_initializer=U.normc_initializer(1.0))[:,0] + + self.state_in = [] + self.state_out = [] + + stochastic = tf.compat.v1.placeholder(dtype=tf.bool, shape=()) + ac = self.pd.sample() # XXX + self._act = U.function([stochastic, ob], [ac, self.vpred]) + + def act(self, stochastic, ob): + ac1, vpred1 = self._act(stochastic, ob[None]) + return ac1[0], vpred1[0] + def get_variables(self): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, self.scope) + def get_trainable_variables(self): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, self.scope) + def get_initial_state(self): + return [] + diff --git a/baselines/ppo1/mlp_policy.py b/baselines/ppo1/mlp_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..e6e2594906218a94dd19a4138da0b0b42ef15547 --- /dev/null +++ b/baselines/ppo1/mlp_policy.py @@ -0,0 +1,61 @@ +from baselines.common.mpi_running_mean_std import RunningMeanStd +import baselines.common.tf_util as U +import tensorflow as tf +import gym +from baselines.common.distributions import make_pdtype + +class MlpPolicy(object): + recurrent = False + def __init__(self, name, *args, **kwargs): + with tf.compat.v1.variable_scope(name): + self._init(*args, **kwargs) + self.scope = tf.compat.v1.get_variable_scope().name + + def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True): + assert isinstance(ob_space, gym.spaces.Box) + + self.pdtype = pdtype = make_pdtype(ac_space) + sequence_length = None + + ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape)) + + with tf.compat.v1.variable_scope("obfilter"): + self.ob_rms = RunningMeanStd(shape=ob_space.shape) + + with tf.compat.v1.variable_scope('vf'): + obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0) + last_out = obz + for i in range(num_hid_layers): + last_out = tf.nn.tanh(tf.compat.v1.layers.dense(last_out, hid_size, name="fc%i"%(i+1), kernel_initializer=U.normc_initializer(1.0))) + self.vpred = tf.compat.v1.layers.dense(last_out, 1, name='final', kernel_initializer=U.normc_initializer(1.0))[:,0] + + with tf.compat.v1.variable_scope('pol'): + last_out = obz + for i in range(num_hid_layers): + last_out = tf.nn.tanh(tf.compat.v1.layers.dense(last_out, hid_size, name='fc%i'%(i+1), kernel_initializer=U.normc_initializer(1.0))) + if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box): + mean = tf.compat.v1.layers.dense(last_out, pdtype.param_shape()[0]//2, name='final', kernel_initializer=U.normc_initializer(0.01)) + logstd = tf.compat.v1.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.compat.v1.zeros_initializer()) + pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1) + else: + pdparam = tf.compat.v1.layers.dense(last_out, pdtype.param_shape()[0], name='final', kernel_initializer=U.normc_initializer(0.01)) + + self.pd = pdtype.pdfromflat(pdparam) + + self.state_in = [] + self.state_out = [] + + stochastic = tf.compat.v1.placeholder(dtype=tf.bool, shape=()) + ac = U.switch(stochastic, self.pd.sample(), self.pd.mode()) + self._act = U.function([stochastic, ob], [ac, self.vpred]) + + def act(self, stochastic, ob): + ac1, vpred1 = self._act(stochastic, ob[None]) + return ac1[0], vpred1[0] + def get_variables(self): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, self.scope) + def get_trainable_variables(self): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, self.scope) + def get_initial_state(self): + return [] + diff --git a/baselines/ppo1/pposgd_simple.py b/baselines/ppo1/pposgd_simple.py new file mode 100644 index 0000000000000000000000000000000000000000..1cce57f9af986f59bfb26ca8a5517371e49a1b41 --- /dev/null +++ b/baselines/ppo1/pposgd_simple.py @@ -0,0 +1,217 @@ +from baselines.common import Dataset, explained_variance, fmt_row, zipsame +from baselines import logger +import baselines.common.tf_util as U +import tensorflow as tf, numpy as np +import time +from baselines.common.mpi_adam import MpiAdam +from baselines.common.mpi_moments import mpi_moments +from mpi4py import MPI +from collections import deque + +def traj_segment_generator(pi, env, horizon, stochastic): + t = 0 + ac = env.action_space.sample() # not used, just so we have the datatype + new = True # marks if we're on first timestep of an episode + ob = env.reset() + + cur_ep_ret = 0 # return in current episode + cur_ep_len = 0 # len of current episode + ep_rets = [] # returns of completed episodes in this segment + ep_lens = [] # lengths of ... + + # Initialize history arrays + obs = np.array([ob for _ in range(horizon)]) + rews = np.zeros(horizon, 'float32') + vpreds = np.zeros(horizon, 'float32') + news = np.zeros(horizon, 'int32') + acs = np.array([ac for _ in range(horizon)]) + prevacs = acs.copy() + + while True: + prevac = ac + ac, vpred = pi.act(stochastic, ob) + # Slight weirdness here because we need value function at time T + # before returning segment [0, T-1] so we get the correct + # terminal value + if t > 0 and t % horizon == 0: + yield {"ob" : obs, "rew" : rews, "vpred" : vpreds, "new" : news, + "ac" : acs, "prevac" : prevacs, "nextvpred": vpred * (1 - new), + "ep_rets" : ep_rets, "ep_lens" : ep_lens} + # Be careful!!! if you change the downstream algorithm to aggregate + # several of these batches, then be sure to do a deepcopy + ep_rets = [] + ep_lens = [] + i = t % horizon + obs[i] = ob + vpreds[i] = vpred + news[i] = new + acs[i] = ac + prevacs[i] = prevac + + ob, rew, new, _ = env.step(ac) + rews[i] = rew + + cur_ep_ret += rew + cur_ep_len += 1 + if new: + ep_rets.append(cur_ep_ret) + ep_lens.append(cur_ep_len) + cur_ep_ret = 0 + cur_ep_len = 0 + ob = env.reset() + t += 1 + +def add_vtarg_and_adv(seg, gamma, lam): + """ + Compute target value using TD(lambda) estimator, and advantage with GAE(lambda) + """ + new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1 + vpred = np.append(seg["vpred"], seg["nextvpred"]) + T = len(seg["rew"]) + seg["adv"] = gaelam = np.empty(T, 'float32') + rew = seg["rew"] + lastgaelam = 0 + for t in reversed(range(T)): + nonterminal = 1-new[t+1] + delta = rew[t] + gamma * vpred[t+1] * nonterminal - vpred[t] + gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam + seg["tdlamret"] = seg["adv"] + seg["vpred"] + +def learn(env, policy_fn, *, + timesteps_per_actorbatch, # timesteps per actor per update + clip_param, entcoeff, # clipping parameter epsilon, entropy coeff + optim_epochs, optim_stepsize, optim_batchsize,# optimization hypers + gamma, lam, # advantage estimation + max_timesteps=0, max_episodes=0, max_iters=0, max_seconds=0, # time constraint + callback=None, # you can do anything in the callback, since it takes locals(), globals() + adam_epsilon=1e-5, + schedule='constant' # annealing for stepsize parameters (epsilon and adam) + ): + # Setup losses and stuff + # ---------------------------------------- + ob_space = env.observation_space + ac_space = env.action_space + pi = policy_fn("pi", ob_space, ac_space) # Construct network for new policy + oldpi = policy_fn("oldpi", ob_space, ac_space) # Network for old policy + atarg = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable) + ret = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None]) # Empirical return + + lrmult = tf.compat.v1.placeholder(name='lrmult', dtype=tf.float32, shape=[]) # learning rate multiplier, updated with schedule + + ob = U.get_placeholder_cached(name="ob") + ac = pi.pdtype.sample_placeholder([None]) + + kloldnew = oldpi.pd.kl(pi.pd) + ent = pi.pd.entropy() + meankl = tf.reduce_mean(input_tensor=kloldnew) + meanent = tf.reduce_mean(input_tensor=ent) + pol_entpen = (-entcoeff) * meanent + + ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold + surr1 = ratio * atarg # surrogate from conservative policy iteration + surr2 = tf.clip_by_value(ratio, 1.0 - clip_param, 1.0 + clip_param) * atarg # + pol_surr = - tf.reduce_mean(input_tensor=tf.minimum(surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP) + vf_loss = tf.reduce_mean(input_tensor=tf.square(pi.vpred - ret)) + total_loss = pol_surr + pol_entpen + vf_loss + losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent] + loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent"] + + var_list = pi.get_trainable_variables() + lossandgrad = U.function([ob, ac, atarg, ret, lrmult], losses + [U.flatgrad(total_loss, var_list)]) + adam = MpiAdam(var_list, epsilon=adam_epsilon) + + assign_old_eq_new = U.function([],[], updates=[tf.compat.v1.assign(oldv, newv) + for (oldv, newv) in zipsame(oldpi.get_variables(), pi.get_variables())]) + compute_losses = U.function([ob, ac, atarg, ret, lrmult], losses) + + U.initialize() + adam.sync() + + # Prepare for rollouts + # ---------------------------------------- + seg_gen = traj_segment_generator(pi, env, timesteps_per_actorbatch, stochastic=True) + + episodes_so_far = 0 + timesteps_so_far = 0 + iters_so_far = 0 + tstart = time.time() + lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths + rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards + + assert sum([max_iters>0, max_timesteps>0, max_episodes>0, max_seconds>0])==1, "Only one time constraint permitted" + + while True: + if callback: callback(locals(), globals()) + if max_timesteps and timesteps_so_far >= max_timesteps: + break + elif max_episodes and episodes_so_far >= max_episodes: + break + elif max_iters and iters_so_far >= max_iters: + break + elif max_seconds and time.time() - tstart >= max_seconds: + break + + if schedule == 'constant': + cur_lrmult = 1.0 + elif schedule == 'linear': + cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0) + else: + raise NotImplementedError + + logger.log("********** Iteration %i ************"%iters_so_far) + + seg = seg_gen.__next__() + add_vtarg_and_adv(seg, gamma, lam) + + # ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets)) + ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"] + vpredbefore = seg["vpred"] # predicted value function before udpate + atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate + d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret), deterministic=pi.recurrent) + optim_batchsize = optim_batchsize or ob.shape[0] + + if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy + + assign_old_eq_new() # set old parameter values to new parameter values + logger.log("Optimizing...") + logger.log(fmt_row(13, loss_names)) + # Here we do a bunch of optimization epochs over the data + for _ in range(optim_epochs): + losses = [] # list of tuples, each of which gives the loss for a minibatch + for batch in d.iterate_once(optim_batchsize): + *newlosses, g = lossandgrad(batch["ob"], batch["ac"], batch["atarg"], batch["vtarg"], cur_lrmult) + adam.update(g, optim_stepsize * cur_lrmult) + losses.append(newlosses) + logger.log(fmt_row(13, np.mean(losses, axis=0))) + + logger.log("Evaluating losses...") + losses = [] + for batch in d.iterate_once(optim_batchsize): + newlosses = compute_losses(batch["ob"], batch["ac"], batch["atarg"], batch["vtarg"], cur_lrmult) + losses.append(newlosses) + meanlosses,_,_ = mpi_moments(losses, axis=0) + logger.log(fmt_row(13, meanlosses)) + for (lossval, name) in zipsame(meanlosses, loss_names): + logger.record_tabular("loss_"+name, lossval) + logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret)) + lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values + listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples + lens, rews = map(flatten_lists, zip(*listoflrpairs)) + lenbuffer.extend(lens) + rewbuffer.extend(rews) + logger.record_tabular("EpLenMean", np.mean(lenbuffer)) + logger.record_tabular("EpRewMean", np.mean(rewbuffer)) + logger.record_tabular("EpThisIter", len(lens)) + episodes_so_far += len(lens) + timesteps_so_far += sum(lens) + iters_so_far += 1 + logger.record_tabular("EpisodesSoFar", episodes_so_far) + logger.record_tabular("TimestepsSoFar", timesteps_so_far) + logger.record_tabular("TimeElapsed", time.time() - tstart) + if MPI.COMM_WORLD.Get_rank()==0: + logger.dump_tabular() + + return pi + +def flatten_lists(listoflists): + return [el for list_ in listoflists for el in list_] diff --git a/baselines/ppo1/run_atari.py b/baselines/ppo1/run_atari.py new file mode 100644 index 0000000000000000000000000000000000000000..96e348243604de1110b0df00a07074582d7e5d24 --- /dev/null +++ b/baselines/ppo1/run_atari.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python3 + +from mpi4py import MPI +from baselines.common import set_global_seeds +from baselines import bench +import os.path as osp +from baselines import logger +from baselines.common.atari_wrappers import make_atari, wrap_deepmind +from baselines.common.cmd_util import atari_arg_parser + +def train(env_id, num_timesteps, seed): + from baselines.ppo1 import pposgd_simple, cnn_policy + import baselines.common.tf_util as U + rank = MPI.COMM_WORLD.Get_rank() + sess = U.single_threaded_session() + sess.__enter__() + if rank == 0: + logger.configure() + else: + logger.configure(format_strs=[]) + workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank() if seed is not None else None + set_global_seeds(workerseed) + env = make_atari(env_id) + def policy_fn(name, ob_space, ac_space): #pylint: disable=W0613 + return cnn_policy.CnnPolicy(name=name, ob_space=ob_space, ac_space=ac_space) + env = bench.Monitor(env, logger.get_dir() and + osp.join(logger.get_dir(), str(rank))) + env.seed(workerseed) + + env = wrap_deepmind(env) + env.seed(workerseed) + + pposgd_simple.learn(env, policy_fn, + max_timesteps=int(num_timesteps * 1.1), + timesteps_per_actorbatch=256, + clip_param=0.2, entcoeff=0.01, + optim_epochs=4, optim_stepsize=1e-3, optim_batchsize=64, + gamma=0.99, lam=0.95, + schedule='linear' + ) + env.close() + +def main(): + args = atari_arg_parser().parse_args() + train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) + +if __name__ == '__main__': + main() diff --git a/baselines/ppo1/run_humanoid.py b/baselines/ppo1/run_humanoid.py new file mode 100644 index 0000000000000000000000000000000000000000..91a6db7cff4f1f48aa43beed5fc8e34418f07c68 --- /dev/null +++ b/baselines/ppo1/run_humanoid.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python3 +import os +from baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser +from baselines.common import tf_util as U +from baselines import logger + +import gym + +def train(num_timesteps, seed, model_path=None): + env_id = 'Humanoid-v2' + from baselines.ppo1 import mlp_policy, pposgd_simple + U.make_session(num_cpu=1).__enter__() + def policy_fn(name, ob_space, ac_space): + return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, + hid_size=64, num_hid_layers=2) + env = make_mujoco_env(env_id, seed) + + # parameters below were the best found in a simple random search + # these are good enough to make humanoid walk, but whether those are + # an absolute best or not is not certain + env = RewScale(env, 0.1) + logger.log("NOTE: reward will be scaled by a factor of 10 in logged stats. Check the monitor for unscaled reward.") + pi = pposgd_simple.learn(env, policy_fn, + max_timesteps=num_timesteps, + timesteps_per_actorbatch=2048, + clip_param=0.1, entcoeff=0.0, + optim_epochs=10, + optim_stepsize=1e-4, + optim_batchsize=64, + gamma=0.99, + lam=0.95, + schedule='constant', + ) + env.close() + if model_path: + U.save_state(model_path) + + return pi + +class RewScale(gym.RewardWrapper): + def __init__(self, env, scale): + gym.RewardWrapper.__init__(self, env) + self.scale = scale + def reward(self, r): + return r * self.scale + +def main(): + logger.configure() + parser = mujoco_arg_parser() + parser.add_argument('--model-path', default=os.path.join(logger.get_dir(), 'humanoid_policy')) + parser.set_defaults(num_timesteps=int(5e7)) + + args = parser.parse_args() + + if not args.play: + # train the model + train(num_timesteps=args.num_timesteps, seed=args.seed, model_path=args.model_path) + else: + # construct the model object, load pre-trained model and render + pi = train(num_timesteps=1, seed=args.seed) + U.load_state(args.model_path) + env = make_mujoco_env('Humanoid-v2', seed=0) + + ob = env.reset() + while True: + action = pi.act(stochastic=False, ob=ob)[0] + ob, _, done, _ = env.step(action) + env.render() + if done: + ob = env.reset() + +if __name__ == '__main__': + main() diff --git a/baselines/ppo1/run_mujoco.py b/baselines/ppo1/run_mujoco.py new file mode 100644 index 0000000000000000000000000000000000000000..638998316b397ede6e945886f7566f806415101a --- /dev/null +++ b/baselines/ppo1/run_mujoco.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python3 + +from baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser +from baselines.common import tf_util as U +from baselines import logger + +def train(env_id, num_timesteps, seed): + from baselines.ppo1 import mlp_policy, pposgd_simple + U.make_session(num_cpu=1).__enter__() + def policy_fn(name, ob_space, ac_space): + return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, + hid_size=64, num_hid_layers=2) + env = make_mujoco_env(env_id, seed) + pposgd_simple.learn(env, policy_fn, + max_timesteps=num_timesteps, + timesteps_per_actorbatch=2048, + clip_param=0.2, entcoeff=0.0, + optim_epochs=10, optim_stepsize=3e-4, optim_batchsize=64, + gamma=0.99, lam=0.95, schedule='linear', + ) + env.close() + +def main(): + args = mujoco_arg_parser().parse_args() + logger.configure() + train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) + +if __name__ == '__main__': + main() diff --git a/baselines/ppo1/run_robotics.py b/baselines/ppo1/run_robotics.py new file mode 100644 index 0000000000000000000000000000000000000000..7d84185a1b8f2ea9000b9ed3ca6b9b3a0de358e3 --- /dev/null +++ b/baselines/ppo1/run_robotics.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 + +from mpi4py import MPI +from baselines.common import set_global_seeds +from baselines import logger +from baselines.common.cmd_util import make_robotics_env, robotics_arg_parser +import mujoco_py + + +def train(env_id, num_timesteps, seed): + from baselines.ppo1 import mlp_policy, pposgd_simple + import baselines.common.tf_util as U + rank = MPI.COMM_WORLD.Get_rank() + sess = U.single_threaded_session() + sess.__enter__() + mujoco_py.ignore_mujoco_warnings().__enter__() + workerseed = seed + 10000 * rank + set_global_seeds(workerseed) + env = make_robotics_env(env_id, workerseed, rank=rank) + def policy_fn(name, ob_space, ac_space): + return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space, + hid_size=256, num_hid_layers=3) + + pposgd_simple.learn(env, policy_fn, + max_timesteps=num_timesteps, + timesteps_per_actorbatch=2048, + clip_param=0.2, entcoeff=0.0, + optim_epochs=5, optim_stepsize=3e-4, optim_batchsize=256, + gamma=0.99, lam=0.95, schedule='linear', + ) + env.close() + + +def main(): + args = robotics_arg_parser().parse_args() + train(args.env, num_timesteps=args.num_timesteps, seed=args.seed) + + +if __name__ == '__main__': + main() diff --git a/baselines/ppo2/README.md b/baselines/ppo2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4d431bca967caffedb5ea76ef38af4b9d99571d8 --- /dev/null +++ b/baselines/ppo2/README.md @@ -0,0 +1,8 @@ +# PPO2 + +- Original paper: https://arxiv.org/abs/1707.06347 +- Baselines blog post: https://blog.openai.com/openai-baselines-ppo/ + +- `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options. +- `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M frames on a Mujoco Ant environment. +- also refer to the repo-wide [README.md](../../README.md#training-models) diff --git a/baselines/ppo2/__init__.py b/baselines/ppo2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/ppo2/defaults.py b/baselines/ppo2/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..9f3f96e976e3f02f5819476f9e81896d5b8e8898 --- /dev/null +++ b/baselines/ppo2/defaults.py @@ -0,0 +1,25 @@ +def mujoco(): + return dict( + nsteps=2048, + nminibatches=32, + lam=0.95, + gamma=0.99, + noptepochs=10, + log_interval=1, + ent_coef=0.0, + lr=lambda f: 3e-4 * f, + cliprange=0.2, + value_network='copy' + ) + +def atari(): + return dict( + nsteps=128, nminibatches=4, + lam=0.95, gamma=0.99, noptepochs=4, log_interval=1, + ent_coef=.01, + lr=lambda f : f * 2.5e-4, + cliprange=0.1, + ) + +def retro(): + return atari() diff --git a/baselines/ppo2/microbatched_model.py b/baselines/ppo2/microbatched_model.py new file mode 100644 index 0000000000000000000000000000000000000000..ecb8e5ad6ef0ef2ee583425490c9b85677fdca8d --- /dev/null +++ b/baselines/ppo2/microbatched_model.py @@ -0,0 +1,78 @@ +import tensorflow as tf +import numpy as np +from baselines.ppo2.model import Model + +class MicrobatchedModel(Model): + """ + Model that does training one microbatch at a time - when gradient computation + on the entire minibatch causes some overflow + """ + def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train, + nsteps, ent_coef, vf_coef, max_grad_norm, mpi_rank_weight, comm, microbatch_size): + + self.nmicrobatches = nbatch_train // microbatch_size + self.microbatch_size = microbatch_size + assert nbatch_train % microbatch_size == 0, 'microbatch_size ({}) should divide nbatch_train ({}) evenly'.format(microbatch_size, nbatch_train) + + super().__init__( + policy=policy, + ob_space=ob_space, + ac_space=ac_space, + nbatch_act=nbatch_act, + nbatch_train=microbatch_size, + nsteps=nsteps, + ent_coef=ent_coef, + vf_coef=vf_coef, + max_grad_norm=max_grad_norm, + mpi_rank_weight=mpi_rank_weight, + comm=comm) + + self.grads_ph = [tf.compat.v1.placeholder(dtype=g.dtype, shape=g.shape) for g in self.grads] + grads_ph_and_vars = list(zip(self.grads_ph, self.var)) + self._apply_gradients_op = self.trainer.apply_gradients(grads_ph_and_vars) + + + def train(self, lr, cliprange, obs, returns, masks, actions, values, neglogpacs, states=None): + assert states is None, "microbatches with recurrent models are not supported yet" + + # Here we calculate advantage A(s,a) = R + yV(s') - V(s) + # Returns = R + yV(s') + advs = returns - values + + # Normalize the advantages + advs = (advs - advs.mean()) / (advs.std() + 1e-8) + + # Initialize empty list for per-microbatch stats like pg_loss, vf_loss, entropy, approxkl (whatever is in self.stats_list) + stats_vs = [] + + for microbatch_idx in range(self.nmicrobatches): + _sli = range(microbatch_idx * self.microbatch_size, (microbatch_idx+1) * self.microbatch_size) + td_map = { + self.train_model.X: obs[_sli], + self.A:actions[_sli], + self.ADV:advs[_sli], + self.R:returns[_sli], + self.CLIPRANGE:cliprange, + self.OLDNEGLOGPAC:neglogpacs[_sli], + self.OLDVPRED:values[_sli] + } + + # Compute gradient on a microbatch (note that variables do not change here) ... + grad_v, stats_v = self.sess.run([self.grads, self.stats_list], td_map) + if microbatch_idx == 0: + sum_grad_v = grad_v + else: + # .. and add to the total of the gradients + for i, g in enumerate(grad_v): + sum_grad_v[i] += g + stats_vs.append(stats_v) + + feed_dict = {ph: sum_g / self.nmicrobatches for ph, sum_g in zip(self.grads_ph, sum_grad_v)} + feed_dict[self.LR] = lr + # Update variables using average of the gradients + self.sess.run(self._apply_gradients_op, feed_dict) + # Return average of the stats + return np.mean(np.array(stats_vs), axis=0).tolist() + + + diff --git a/baselines/ppo2/model.py b/baselines/ppo2/model.py new file mode 100644 index 0000000000000000000000000000000000000000..11875b2b01d4cad9fac5dac465ddae8e8d63bcf9 --- /dev/null +++ b/baselines/ppo2/model.py @@ -0,0 +1,159 @@ +import tensorflow as tf +import functools + +from baselines.common.tf_util import get_session, save_variables, load_variables +from baselines.common.tf_util import initialize + +try: + from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer + from mpi4py import MPI + from baselines.common.mpi_util import sync_from_root +except ImportError: + MPI = None + +class Model(object): + """ + We use this object to : + __init__: + - Creates the step_model + - Creates the train_model + + train(): + - Make the training part (feedforward and retropropagation of gradients) + + save/load(): + - Save load the model + """ + def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train, + nsteps, ent_coef, vf_coef, max_grad_norm, mpi_rank_weight=1, comm=None, microbatch_size=None): + self.sess = sess = get_session() + + if MPI is not None and comm is None: + comm = MPI.COMM_WORLD + + with tf.compat.v1.variable_scope('ppo2_model', reuse=tf.compat.v1.AUTO_REUSE): + # CREATE OUR TWO MODELS + # act_model that is used for sampling + act_model = policy(nbatch_act, 1, sess) + + # Train model for training + if microbatch_size is None: + train_model = policy(nbatch_train, nsteps, sess) + else: + train_model = policy(microbatch_size, nsteps, sess) + + # CREATE THE PLACEHOLDERS + self.A = A = train_model.pdtype.sample_placeholder([None]) + self.ADV = ADV = tf.compat.v1.placeholder(tf.float32, [None]) + self.R = R = tf.compat.v1.placeholder(tf.float32, [None]) + # Keep track of old actor + self.OLDNEGLOGPAC = OLDNEGLOGPAC = tf.compat.v1.placeholder(tf.float32, [None]) + # Keep track of old critic + self.OLDVPRED = OLDVPRED = tf.compat.v1.placeholder(tf.float32, [None]) + self.LR = LR = tf.compat.v1.placeholder(tf.float32, []) + # Cliprange + self.CLIPRANGE = CLIPRANGE = tf.compat.v1.placeholder(tf.float32, []) + + neglogpac = train_model.pd.neglogp(A) + + # Calculate the entropy + # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy. + entropy = tf.reduce_mean(input_tensor=train_model.pd.entropy()) + + # CALCULATE THE LOSS + # Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss + + # Clip the value to reduce variability during Critic training + # Get the predicted value + vpred = train_model.vf + vpredclipped = OLDVPRED + tf.clip_by_value(train_model.vf - OLDVPRED, - CLIPRANGE, CLIPRANGE) + # Unclipped value + vf_losses1 = tf.square(vpred - R) + # Clipped value + vf_losses2 = tf.square(vpredclipped - R) + + vf_loss = .5 * tf.reduce_mean(input_tensor=tf.maximum(vf_losses1, vf_losses2)) + + # Calculate ratio (pi current policy / pi old policy) + ratio = tf.exp(OLDNEGLOGPAC - neglogpac) + + # Defining Loss = - J is equivalent to max J + pg_losses = -ADV * ratio + + pg_losses2 = -ADV * tf.clip_by_value(ratio, 1.0 - CLIPRANGE, 1.0 + CLIPRANGE) + + # Final PG loss + pg_loss = tf.reduce_mean(input_tensor=tf.maximum(pg_losses, pg_losses2)) + approxkl = .5 * tf.reduce_mean(input_tensor=tf.square(neglogpac - OLDNEGLOGPAC)) + clipfrac = tf.reduce_mean(input_tensor=tf.cast(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE), dtype=tf.float32)) + + # Total loss + loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef + + # UPDATE THE PARAMETERS USING LOSS + # 1. Get the model parameters + params = tf.compat.v1.trainable_variables('ppo2_model') + # 2. Build our trainer + if comm is not None and comm.Get_size() > 1: + self.trainer = MpiAdamOptimizer(comm, learning_rate=LR, mpi_rank_weight=mpi_rank_weight, epsilon=1e-5) + else: + self.trainer = tf.compat.v1.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5) + # 3. Calculate the gradients + grads_and_var = self.trainer.compute_gradients(loss, params) + grads, var = zip(*grads_and_var) + + if max_grad_norm is not None: + # Clip the gradients (normalize) + grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm) + grads_and_var = list(zip(grads, var)) + # zip aggregate each gradient with parameters associated + # For instance zip(ABCD, xyza) => Ax, By, Cz, Da + + self.grads = grads + self.var = var + self._train_op = self.trainer.apply_gradients(grads_and_var) + self.loss_names = ['policy_loss', 'value_loss', 'policy_entropy', 'approxkl', 'clipfrac'] + self.stats_list = [pg_loss, vf_loss, entropy, approxkl, clipfrac] + + + self.train_model = train_model + self.act_model = act_model + self.step = act_model.step + self.value = act_model.value + self.initial_state = act_model.initial_state + + self.save = functools.partial(save_variables, sess=sess) + self.load = functools.partial(load_variables, sess=sess) + + initialize() + global_variables = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope="") + if MPI is not None: + sync_from_root(sess, global_variables, comm=comm) #pylint: disable=E1101 + + def train(self, lr, cliprange, obs, returns, masks, actions, values, neglogpacs, states=None): + # Here we calculate advantage A(s,a) = R + yV(s') - V(s) + # Returns = R + yV(s') + advs = returns - values + + # Normalize the advantages + advs = (advs - advs.mean()) / (advs.std() + 1e-8) + + td_map = { + self.train_model.X : obs, + self.A : actions, + self.ADV : advs, + self.R : returns, + self.LR : lr, + self.CLIPRANGE : cliprange, + self.OLDNEGLOGPAC : neglogpacs, + self.OLDVPRED : values + } + if states is not None: + td_map[self.train_model.S] = states + td_map[self.train_model.M] = masks + + return self.sess.run( + self.stats_list + [self._train_op], + td_map + )[:-1] + diff --git a/baselines/ppo2/ppo2.py b/baselines/ppo2/ppo2.py new file mode 100644 index 0000000000000000000000000000000000000000..f8eb30ba4e2e55e95b2237a9db8dbb08e4b9b604 --- /dev/null +++ b/baselines/ppo2/ppo2.py @@ -0,0 +1,232 @@ +import os +import time +import numpy as np +import os.path as osp +from baselines import logger +from collections import deque +from baselines.common import explained_variance, set_global_seeds +from baselines.common.policies import build_policy +try: + from mpi4py import MPI +except ImportError: + MPI = None +from baselines.ppo2.runner import Runner + + +def constfn(val): + def f(_): + return val + return f + +def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4, + vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95, + log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2, + save_interval=0, load_path=None, model_fn=None, update_fn=None, init_fn=None, mpi_rank_weight=1, comm=None, **network_kwargs): + ''' + Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347) + + Parameters: + ---------- + + network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list) + specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns + tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward + neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets. + See common/models.py/lstm for more details on using recurrent nets in policies + + env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation. + The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class. + + + nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where + nenv is number of environment copies simulated in parallel) + + total_timesteps: int number of timesteps (i.e. number of actions taken in the environment) + + ent_coef: float policy entropy coefficient in the optimization objective + + lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the + training and 0 is the end of the training. + + vf_coef: float value function loss coefficient in the optimization objective + + max_grad_norm: float or None gradient norm clipping coefficient + + gamma: float discounting factor + + lam: float advantage estimation discounting factor (lambda in the paper) + + log_interval: int number of timesteps between logging events + + nminibatches: int number of training minibatches per update. For recurrent policies, + should be smaller or equal than number of environments run in parallel. + + noptepochs: int number of training epochs per update + + cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training + and 0 is the end of the training + + save_interval: int number of timesteps between saving events + + load_path: str path to load the model from + + **network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network + For instance, 'mlp' network architecture has arguments num_hidden and num_layers. + + + + ''' + + set_global_seeds(seed) + + if isinstance(lr, float): lr = constfn(lr) + else: assert callable(lr) + if isinstance(cliprange, float): cliprange = constfn(cliprange) + else: assert callable(cliprange) + total_timesteps = int(total_timesteps) + + policy = build_policy(env, network, **network_kwargs) + + # Get the nb of env + nenvs = env.num_envs + + # Get state_space and action_space + ob_space = env.observation_space + ac_space = env.action_space + + # Calculate the batch_size + nbatch = nenvs * nsteps + nbatch_train = nbatch // nminibatches + is_mpi_root = (MPI is None or MPI.COMM_WORLD.Get_rank() == 0) + + # Instantiate the model object (that creates act_model and train_model) + if model_fn is None: + from baselines.ppo2.model import Model + model_fn = Model + + model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train, + nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef, + max_grad_norm=max_grad_norm, comm=comm, mpi_rank_weight=mpi_rank_weight) + + if load_path is not None: + model.load(load_path) + # Instantiate the runner object + runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam) + if eval_env is not None: + eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam) + + epinfobuf = deque(maxlen=100) + if eval_env is not None: + eval_epinfobuf = deque(maxlen=100) + + if init_fn is not None: + init_fn() + + # Start total timer + tfirststart = time.perf_counter() + + nupdates = total_timesteps//nbatch + for update in range(1, nupdates+1): + assert nbatch % nminibatches == 0 + # Start timer + tstart = time.perf_counter() + frac = 1.0 - (update - 1.0) / nupdates + # Calculate the learning rate + lrnow = lr(frac) + # Calculate the cliprange + cliprangenow = cliprange(frac) + + if update % log_interval == 0 and is_mpi_root: logger.info('Stepping environment...') + + # Get minibatch + obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632 + if eval_env is not None: + eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() #pylint: disable=E0632 + + if update % log_interval == 0 and is_mpi_root: logger.info('Done.') + + epinfobuf.extend(epinfos) + if eval_env is not None: + eval_epinfobuf.extend(eval_epinfos) + + # Here what we're going to do is for each minibatch calculate the loss and append it. + mblossvals = [] + if states is None: # nonrecurrent version + # Index of each element of batch_size + # Create the indices array + inds = np.arange(nbatch) + for _ in range(noptepochs): + # Randomize the indexes + np.random.shuffle(inds) + # 0 to batch_size with batch_train_size step + for start in range(0, nbatch, nbatch_train): + end = start + nbatch_train + mbinds = inds[start:end] + slices = (arr[mbinds] for arr in (obs, returns, masks, actions, values, neglogpacs)) + mblossvals.append(model.train(lrnow, cliprangenow, *slices)) + else: # recurrent version + assert nenvs % nminibatches == 0 + envsperbatch = nenvs // nminibatches + envinds = np.arange(nenvs) + flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps) + for _ in range(noptepochs): + np.random.shuffle(envinds) + for start in range(0, nenvs, envsperbatch): + end = start + envsperbatch + mbenvinds = envinds[start:end] + mbflatinds = flatinds[mbenvinds].ravel() + slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs)) + mbstates = states[mbenvinds] + mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates)) + + # Feedforward --> get losses --> update + lossvals = np.mean(mblossvals, axis=0) + # End timer + tnow = time.perf_counter() + # Calculate the fps (frame per second) + fps = int(nbatch / (tnow - tstart)) + + if update_fn is not None: + update_fn(update) + + if update % log_interval == 0 or update == 1: + # Calculates if value function is a good predicator of the returns (ev > 1) + # or if it's just worse than predicting nothing (ev =< 0) + ev = explained_variance(values, returns) + logger.logkv("misc/serial_timesteps", update*nsteps) + logger.logkv("misc/nupdates", update) + logger.logkv("misc/total_timesteps", update*nbatch) + logger.logkv("fps", fps) + logger.logkv("misc/explained_variance", float(ev)) + logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf])) + logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf])) + if eval_env is not None: + logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]) ) + logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]) ) + logger.logkv('misc/time_elapsed', tnow - tfirststart) + for (lossval, lossname) in zip(lossvals, model.loss_names): + logger.logkv('loss/' + lossname, lossval) + + logger.dumpkvs() + if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and is_mpi_root: + checkdir = osp.join(logger.get_dir(), 'checkpoints') + os.makedirs(checkdir, exist_ok=True) + savepath = osp.join(checkdir, '%.5i'%update) + print('Saving to', savepath) + model.save(savepath) + + # save the trained model + if logger.get_dir() and is_mpi_root: + checkdir = osp.join(logger.get_dir(), 'checkpoints') + os.makedirs(checkdir, exist_ok=True) + savepath = osp.join(checkdir, '%i' % total_timesteps) + print('Saving to', savepath) + model.save(savepath) + + return model +# Avoid division error when calculate the mean (in our case if epinfo is empty returns np.nan, not return an error) +def safemean(xs): + return np.nan if len(xs) == 0 else np.mean(xs) + + + diff --git a/baselines/ppo2/runner.py b/baselines/ppo2/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..5a30505fd622494df6e3facf4c02c4fa8c90eaf0 --- /dev/null +++ b/baselines/ppo2/runner.py @@ -0,0 +1,76 @@ +import numpy as np +from baselines.common.runners import AbstractEnvRunner + +class Runner(AbstractEnvRunner): + """ + We use this object to make a mini batch of experiences + __init__: + - Initialize the runner + + run(): + - Make a mini batch + """ + def __init__(self, *, env, model, nsteps, gamma, lam): + super().__init__(env=env, model=model, nsteps=nsteps) + # Lambda used in GAE (General Advantage Estimation) + self.lam = lam + # Discount rate + self.gamma = gamma + + def run(self): + # Here, we init the lists that will contain the mb of experiences + mb_obs, mb_rewards, mb_actions, mb_values, mb_dones, mb_neglogpacs = [],[],[],[],[],[] + mb_states = self.states + epinfos = [] + # For n in range number of steps + for _ in range(self.nsteps): + # Given observations, get action value and neglopacs + # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init + actions, values, self.states, neglogpacs = self.model.step(self.obs, S=self.states, M=self.dones) + mb_obs.append(self.obs.copy()) + mb_actions.append(actions) + mb_values.append(values) + mb_neglogpacs.append(neglogpacs) + mb_dones.append(self.dones) + + # Take actions in env and look the results + # Infos contains a ton of useful informations + self.obs[:], rewards, self.dones, infos = self.env.step(actions) + for info in infos: + maybeepinfo = info.get('episode') + if maybeepinfo: epinfos.append(maybeepinfo) + mb_rewards.append(rewards) + #batch of steps to batch of rollouts + mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype) + mb_rewards = np.asarray(mb_rewards, dtype=np.float32) + mb_actions = np.asarray(mb_actions) + mb_values = np.asarray(mb_values, dtype=np.float32) + mb_neglogpacs = np.asarray(mb_neglogpacs, dtype=np.float32) + mb_dones = np.asarray(mb_dones, dtype=np.bool) + last_values = self.model.value(self.obs, S=self.states, M=self.dones) + + # discount/bootstrap off value fn + mb_returns = np.zeros_like(mb_rewards) + mb_advs = np.zeros_like(mb_rewards) + lastgaelam = 0 + for t in reversed(range(self.nsteps)): + if t == self.nsteps - 1: + nextnonterminal = 1.0 - self.dones + nextvalues = last_values + else: + nextnonterminal = 1.0 - mb_dones[t+1] + nextvalues = mb_values[t+1] + delta = mb_rewards[t] + self.gamma * nextvalues * nextnonterminal - mb_values[t] + mb_advs[t] = lastgaelam = delta + self.gamma * self.lam * nextnonterminal * lastgaelam + mb_returns = mb_advs + mb_values + return (*map(sf01, (mb_obs, mb_returns, mb_dones, mb_actions, mb_values, mb_neglogpacs)), + mb_states, epinfos) +# obs, returns, masks, actions, values, neglogpacs, states = runner.run() +def sf01(arr): + """ + swap and then flatten axes 0 and 1 + """ + s = arr.shape + return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:]) + + diff --git a/baselines/ppo2/smodel.py b/baselines/ppo2/smodel.py new file mode 100644 index 0000000000000000000000000000000000000000..63a9e9ac0f5d1ad6178f18fd359c943a206b4382 --- /dev/null +++ b/baselines/ppo2/smodel.py @@ -0,0 +1,234 @@ +import tensorflow as tf +import functools + +from baselines.common.tf_util import get_session, save_variables, load_variables +from baselines.common.tf_util import initialize +import numpy as np +from itertools import combinations + +try: + from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer + from mpi4py import MPI + from baselines.common.mpi_util import sync_from_root +except ImportError: + MPI = None + + +class Model(object): + """ + We use this object to : + __init__: + - Creates the step_model + - Creates the train_model + + train(): + - Make the training part (feedforward and retropropagation of gradients) + + save/load(): + - Save load the model + """ + + def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train, + nsteps, ent_coef, vf_coef, skill_coef, vq_coef, max_grad_norm, alpha1=20, alpha2=1, beta=0.25, + Cd=5, mpi_rank_weight=1, comm=None, + microbatch_size=None): + self.sess = sess = get_session() + + if MPI is not None and comm is None: + comm = MPI.COMM_WORLD + + with tf.compat.v1.variable_scope('sppo2_model', reuse=tf.compat.v1.AUTO_REUSE): + # CREATE OUR TWO MODELS + # act_model that is used for sampling + act_model = policy(nbatch_act, 1, sess) + + # Train model for training + if microbatch_size is None: + train_model = policy(nbatch_train, nsteps, sess) + else: + train_model = policy(microbatch_size, nsteps, sess) + + # CREATE THE PLACEHOLDERS + self.A = A = train_model.pdtype.sample_placeholder([None]) + self.ADV = ADV = tf.compat.v1.placeholder(tf.float32, [None]) + self.R = R = tf.compat.v1.placeholder(tf.float32, [None]) + self.skill_weight = skill_weight = tf.compat.v1.placeholder(tf.float32, []) + + # Keep track of old actor + self.OLDNEGLOGPAC = OLDNEGLOGPAC = tf.compat.v1.placeholder(tf.float32, [None]) + # Keep track of old critic + self.OLDVPRED = OLDVPRED = tf.compat.v1.placeholder(tf.float32, [None]) + self.LR = LR = tf.compat.v1.placeholder(tf.float32, []) + # Cliprange + self.CLIPRANGE = CLIPRANGE = tf.compat.v1.placeholder(tf.float32, []) + + self.train_skill = train_skill = tf.compat.v1.placeholder(tf.bool, []) + + neglogpac = train_model.pd.neglogp(A) + + # Calculate the entropy + # Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy. + entropy = tf.reduce_mean(input_tensor=train_model.pd.entropy()) + + # CALCULATE THE LOSS + # Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss + + # Clip the value to reduce variability during Critic training + # Get the predicted value + vpred = train_model.vf + vpredclipped = OLDVPRED + tf.clip_by_value(train_model.vf - OLDVPRED, - CLIPRANGE, CLIPRANGE) + # Unclipped value + vf_losses1 = tf.square(vpred - R) + # Clipped value + vf_losses2 = tf.square(vpredclipped - R) + + vf_loss = .5 * tf.reduce_mean(input_tensor=tf.maximum(vf_losses1, vf_losses2)) + + # Calculate ratio (pi current policy / pi old policy) + ratio = tf.exp(OLDNEGLOGPAC - neglogpac) + + # Defining Loss = - J is equivalent to max J + pg_losses = -ADV * ratio + + pg_losses2 = -ADV * tf.clip_by_value(ratio, 1.0 - CLIPRANGE, 1.0 + CLIPRANGE) + + # calculating the skill loss + # To avoid the GPU memory exploded, please choose the proper chunk size for your GPUs + chunk_size = 64 + + def pairwise_sqd_distance(x): + if x.shape[-1] <= chunk_size: + tiled = tf.tile(tf.expand_dims(x, axis=1), tf.stack([1, x.shape[0], 1])) + tiled_trans = tf.transpose(tiled, perm=[1, 0, 2]) + diffs = tiled - tiled_trans + sqd_dist_mat = tf.reduce_sum(tf.square(diffs), axis=2) + else: + sqd_dist_mat = [] + for i in range(x.shape[-1] // chunk_size): + tiled = tf.tile(tf.expand_dims(x[:, i * chunk_size:i * chunk_size + chunk_size], axis=1), + tf.stack([1, x.shape[0], 1])) + + tiled_trans = tf.transpose(tiled, perm=[1, 0, 2]) + + diffs = tiled - tiled_trans + if sqd_dist_mat != []: + sqd_dist_mat = tf.add(sqd_dist_mat, tf.reduce_sum(tf.square(diffs), axis=2)) + else: + sqd_dist_mat = tf.reduce_sum(tf.square(diffs), axis=2) + + return sqd_dist_mat + + def make_q(z, alpha): + sqd_dist_mat = pairwise_sqd_distance(z) + q = tf.pow((1 + sqd_dist_mat / alpha), -(alpha + 1) / 2) + q = tf.linalg.set_diag(q, np.zeros(shape=[z.shape[0]])) + q = q / tf.reduce_sum(q, axis=0, keepdims=True) + q = tf.clip_by_value(q, 1e-10, 1.0) + + return q + + p = make_q(train_model.pure_latent, alpha=alpha1) + q = make_q(train_model.skill_latent, alpha=alpha2) + skill_losses = tf.reduce_sum(-(tf.multiply(p, tf.math.log(q)))) / (p.shape[0] * p.shape[1]) + skill_loss_ = tf.reduce_mean(skill_losses) + + # calculating the vq loss + if beta != 0: + commitment_loss = beta * tf.reduce_mean( + input_tensor=(tf.stop_gradient(train_model.pure_vq_latent) - train_model.skill_latent) ** 2, axis=1 + ) + else: + commitment_loss = 0 + codebook_loss = tf.reduce_mean( + input_tensor=(train_model.pure_vq_latent - tf.stop_gradient(train_model.skill_latent)) ** 2, axis=1) + + vq_losses = commitment_loss + codebook_loss + vq_loss_ = tf.reduce_mean(vq_losses) + + # Final PG loss + pg_loss = tf.reduce_mean(input_tensor=tf.maximum(pg_losses, pg_losses2)) + approxkl = .5 * tf.reduce_mean(input_tensor=tf.square(neglogpac - OLDNEGLOGPAC)) + clipfrac = tf.reduce_mean(input_tensor=tf.cast(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE), dtype=tf.float32)) + + # Total loss + + loss = tf.cond(train_skill, lambda: pg_loss - entropy * ent_coef + vf_loss * vf_coef + skill_weight * ( + skill_loss_ * skill_coef + vq_loss_ * vq_coef), + lambda: pg_loss - entropy * ent_coef + vf_loss * vf_coef) + + # UPDATE THE PARAMETERS USING LOSS + # 1. Get the model parameters + params = tf.compat.v1.trainable_variables('sppo2_model') + # 2. Build our trainer + if comm is not None and comm.Get_size() > 1: + self.trainer = MpiAdamOptimizer(comm, learning_rate=LR, mpi_rank_weight=mpi_rank_weight, epsilon=1e-5) + else: + self.trainer = tf.compat.v1.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5) + # 3. Calculate the gradients + grads_and_var = self.trainer.compute_gradients(loss, params) + grads, var = zip(*grads_and_var) + + if max_grad_norm is not None: + # Clip the gradients (normalize) + grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm) + grads_and_var = list(zip(grads, var)) + # zip aggregate each gradient with parameters associated + # For instance zip(ABCD, xyza) => Ax, By, Cz, Da + + self.grads = grads + self.var = var + self._train_op = self.trainer.apply_gradients(grads_and_var) + self.loss_names = ['loss', 'policy_loss', 'value_loss', 'skill_loss', 'vq_loss', + 'policy_entropy', + 'approxkl', + 'clipfrac'] + self.stats_list = [loss, pg_loss, vf_loss, skill_loss_, vq_loss_, entropy, approxkl, + clipfrac] + + self.train_model = train_model + self.act_model = act_model + self.step = act_model.step + self.skill_step = act_model.skill_step + self.value = act_model.value + self.initial_state = act_model.initial_state + + self.save = functools.partial(save_variables, sess=sess) + self.load = functools.partial(load_variables, sess=sess) + + initialize() + global_variables = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope="") + if MPI is not None: + sync_from_root(sess, global_variables, comm=comm) # pylint: disable=E1101 + + def train(self, lr, cliprange, train_skill, skill_weight, obs, returns, + masks, actions, + values, + neglogpacs, + states=None): + # Here we calculate advantage A(s,a) = R + yV(s') - V(s) + # Returns = R + yV(s') + advs = returns - values + + # Normalize the advantages + advs = (advs - advs.mean()) / (advs.std() + 1e-8) + + td_map = { + self.train_model.X: obs, + self.A: actions, + self.ADV: advs, + self.R: returns, + self.LR: lr, + self.train_skill: train_skill, + self.skill_weight: skill_weight, + self.CLIPRANGE: cliprange, + self.OLDNEGLOGPAC: neglogpacs, + self.OLDVPRED: values + } + if states is not None: + td_map[self.train_model.S] = states + td_map[self.train_model.M] = masks + + return self.sess.run( + self.stats_list + [self._train_op], + td_map + )[:-1] diff --git a/baselines/ppo2/sppo2.py b/baselines/ppo2/sppo2.py new file mode 100644 index 0000000000000000000000000000000000000000..9a5665bdcd6a0d5ee1857d67256104516f089416 --- /dev/null +++ b/baselines/ppo2/sppo2.py @@ -0,0 +1,309 @@ +import os +import time +import numpy as np +import os.path as osp +from baselines import logger +from collections import deque +from baselines.common import explained_variance, set_global_seeds +from baselines.common.spolicies import build_policy + +try: + from mpi4py import MPI +except ImportError: + MPI = None +from baselines.ppo2.srunner import Runner + + +def constfn(val): + def f(_): + return val + + return f + + +def learn(*, network, env, total_timesteps, eval_env=None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4, + vf_coef=0.5, skill_coef=500, vq_coef=1, max_grad_norm=0.5, gamma=0.99, lam=0.95, + log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2, alpha1=20, alpha2=1, + num_embeddings=8, beta=0.25, + save_interval=0, load_path=None, model_fn=None, update_fn=None, init_fn=None, mpi_rank_weight=1, comm=None, + highest_score=0, + **network_kwargs): + ''' + Learn policy using Skill PPO algorithm + + Parameters: + ---------- + + network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list) + specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns + tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward + neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets. + See common/models.py/lstm for more details on using recurrent nets in policies + + env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation. + The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class. + + + nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where + nenv is number of environment copies simulated in parallel) + + total_timesteps: int number of timesteps (i.e. number of actions taken in the environment) + + ent_coef: float policy entropy coefficient in the optimization objective + + lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the + training and 0 is the end of the training. + + vf_coef: float value function loss coefficient in the optimization objective + + vq_coef: float vq encoding loss coefficient in the optimization objective + + skill_coef: float skill loss coefficient in the optimization objective + + skill_start_threshold: float partition of the total training steps when training skills + + skill_plateau_threshold: float partition of the total training steps when the weight of skills training + increases to 1 + + max_grad_norm: float or None gradient norm clipping coefficient + + gamma: float discounting factor + + lam: float advantage estimation discounting factor (lambda in the paper) + + log_interval: int number of timesteps between logging events + + nminibatches: int number of training minibatches per update. For recurrent policies, + should be smaller or equal than number of environments run in parallel. + + noptepochs: int number of training epochs per update + + cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training + and 0 is the end of the training + + save_interval: int number of timesteps between saving events + + load_path: str path to load the model from + + **network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network + For instance, 'mlp' network architecture has arguments num_hidden and num_layers. + + + + ''' + + set_global_seeds(seed) + + if isinstance(lr, float): + lr = constfn(lr) + else: + assert callable(lr) + if isinstance(cliprange, float): + cliprange = constfn(cliprange) + else: + assert callable(cliprange) + total_timesteps = int(total_timesteps) + + policy = build_policy(env, network, **network_kwargs) + + # Get the nb of env + nenvs = env.num_envs + + # Get state_space and action_space + ob_space = env.observation_space + ac_space = env.action_space + + # Calculate the batch_size + nbatch = nenvs * nsteps + nbatch_train = nbatch // nminibatches + is_mpi_root = (MPI is None or MPI.COMM_WORLD.Get_rank() == 0) + + # Instantiate the model object (that creates act_model and train_model) + if model_fn is None: + from baselines.ppo2.smodel import Model + model_fn = Model + + model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train, + nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef, skill_coef=skill_coef, vq_coef=vq_coef, + beta=beta, alpha1=alpha1, alpha2=alpha2, + max_grad_norm=max_grad_norm, comm=comm, mpi_rank_weight=mpi_rank_weight) + + if load_path is not None: + model.load(load_path) + # Instantiate the runner object + runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam, num_embeddings=num_embeddings) + if eval_env is not None: + eval_runner = Runner(env=eval_env, model=model, nsteps=nsteps, gamma=gamma, lam=lam, + num_embeddings=num_embeddings) + + epinfobuf = deque(maxlen=100) + if eval_env is not None: + eval_epinfobuf = deque(maxlen=100) + + if init_fn is not None: + init_fn() + + # Start total timer + tfirststart = time.perf_counter() + + train_skill = True + skill_weight = 0.0 + mean_score = 0 + + nupdates = total_timesteps // nbatch + for update in range(1, nupdates + 1): + assert nbatch % nminibatches == 0 + # Start timer + tstart = time.perf_counter() + frac = 1.0 - (update - 1.0) / nupdates + # Calculate the learning rate + lrnow = lr(frac) + # Calculate the cliprange + cliprangenow = cliprange(frac) + + if highest_score != 0: + if update % 50 == 0: + skill_weight = min(mean_score / (0.8 * highest_score), 1) + else: + skill_weight = 1 + + if update % log_interval == 0 and is_mpi_root: logger.info('Stepping environment...') + + # Get minibatch + obs, returns, encoding_indices, masks, actions, values, neglogpacs, states, epinfos = runner.run() # pylint: disable=E0632 + if eval_env is not None: + eval_obs, eval_returns, eval_encoding_indices, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() # pylint: disable=E0632 + + if update % log_interval == 0 and is_mpi_root: logger.info('Done.') + + epinfobuf.extend(epinfos) + if eval_env is not None: + eval_epinfobuf.extend(eval_epinfos) + + # Here what we're going to do is for each minibatch calculate the loss and append it. + mblossvals = [] + if states is None: # nonrecurrent version + # Index of each element of batch_size + # Create the indices array + inds = np.arange(nbatch) + for _ in range(noptepochs): + # Randomize the indexes + np.random.shuffle(inds) + # 0 to batch_size with batch_train_size step + for start in range(0, nbatch, nbatch_train): + end = start + nbatch_train + mbinds = inds[start:end] + slices = (arr[mbinds] for arr in + (obs, returns, masks, + actions, values, neglogpacs)) + mblossvals.append(model.train(lrnow, cliprangenow, train_skill, skill_weight, *slices)) + else: # recurrent version + assert nenvs % nminibatches == 0 + envsperbatch = nenvs // nminibatches + envinds = np.arange(nenvs) + flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps) + for _ in range(noptepochs): + np.random.shuffle(envinds) + for start in range(0, nenvs, envsperbatch): + end = start + envsperbatch + mbenvinds = envinds[start:end] + mbflatinds = flatinds[mbenvinds].ravel() + slices = (arr[mbflatinds] for arr in + (obs, returns, masks, + actions, values, neglogpacs)) + mbstates = states[mbenvinds] + mblossvals.append(model.train(lrnow, cliprangenow, train_skill, skill_weight, *slices, mbstates)) + + # Feedforward --> get losses --> update + lossvals = np.mean(mblossvals, axis=0) + # End timer + tnow = time.perf_counter() + # Calculate the fps (frame per second) + fps = int(nbatch / (tnow - tstart)) + + if update_fn is not None: + update_fn(update) + + if update % log_interval == 0 or update == 1: + # Calculates if value function is a good predicator of the returns (ev > 1) + # or if it's just worse than predicting nothing (ev =< 0) + ev = explained_variance(values, returns) + logger.logkv("misc/serial_timesteps", update * nsteps) + logger.logkv("misc/nupdates", update) + logger.logkv("misc/total_timesteps", update * nbatch) + logger.logkv("fps", fps) + logger.logkv("misc/explained_variance", float(ev)) + mean_score = safemean([epinfo['r'] for epinfo in epinfobuf]) + logger.logkv('eprewmean', mean_score) + logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf])) + if eval_env is not None: + logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf])) + logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf])) + logger.logkv('misc/time_elapsed', tnow - tfirststart) + for (lossval, lossname) in zip(lossvals, model.loss_names): + logger.logkv('loss/' + lossname, lossval) + + logger.dumpkvs() + if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and is_mpi_root: + checkdir = osp.join(logger.get_dir(), 'checkpoints') + os.makedirs(checkdir, exist_ok=True) + savepath = osp.join(checkdir, '%.5i' % update) + print('Saving to', savepath) + model.save(savepath) + + # save the trained model + if logger.get_dir() and is_mpi_root: + checkdir = osp.join(logger.get_dir(), 'checkpoints') + os.makedirs(checkdir, exist_ok=True) + savepath = osp.join(checkdir, '%i' % total_timesteps) + print('Saving to', savepath) + model.save(savepath) + + return model + + +def load(*, network, env, seed=None, nsteps=2048, ent_coef=0.0, + vf_coef=0.5, skill_coef=10, vq_coef=1, max_grad_norm=0.5, nminibatches=4, + beta=0.25, alpha1=20, alpha2=1, + load_path=None, model_fn=None, mpi_rank_weight=1, comm=None, + **network_kwargs): + set_global_seeds(seed) + + policy = build_policy(env, network, **network_kwargs) + + # Get the nb of env + nenvs = env.num_envs + + # Get state_space and action_space + ob_space = env.observation_space + ac_space = env.action_space + + # Calculate the batch_size + nbatch = nenvs * nsteps + nbatch_train = nbatch // nminibatches + + # Instantiate the model object (that creates act_model and train_model) + if model_fn is None: + from baselines.ppo2.smodel import Model + model_fn = Model + + model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train, + nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef, skill_coef=skill_coef, vq_coef=vq_coef, + beta=beta, alpha1=alpha1, alpha2=alpha2, + max_grad_norm=max_grad_norm, comm=comm, mpi_rank_weight=mpi_rank_weight) + + if load_path is not None: + model.load(load_path) + try: + model.load(load_path) + except: + raise Exception('Load path is invalid!') + else: + raise Exception('Load path is empty!') + + return model + + +# Avoid division error when calculate the mean (in our case if epinfo is empty returns np.nan, not return an error) +def safemean(xs): + return np.nan if len(xs) == 0 else np.mean(xs) diff --git a/baselines/ppo2/srunner.py b/baselines/ppo2/srunner.py new file mode 100644 index 0000000000000000000000000000000000000000..857ac7633139a5cf3c89d382925220e96efe9ed1 --- /dev/null +++ b/baselines/ppo2/srunner.py @@ -0,0 +1,92 @@ +import numpy as np +from baselines.common.runners import AbstractEnvRunner +from collections import deque + + +class Runner(AbstractEnvRunner): + """ + We use this object to make a mini batch of experiences + __init__: + - Initialize the runner + + run(): + - Make a mini batch + """ + + def __init__(self, *, env, model, nsteps, gamma, lam, num_embeddings): + super().__init__(env=env, model=model, nsteps=nsteps) + # Lambda used in GAE (General Advantage Estimation) + self.lam = lam + # Discount rate + self.gamma = gamma + self.num_embeddings = num_embeddings + + def run(self): + # Here, we init the lists that will contain the mb of experiences + mb_obs, mb_rewards, mb_actions, mb_values, mb_cluster_value_losses, mb_encoding_indices, mb_dones, mb_neglogpacs = [], [], [], [], [], [], [], [] + mb_states = self.states + epinfos = [] + # For n in range number of steps + for _ in range(self.nsteps): + # Given observations, get action value and neglopacs + # We already have self.obs because Runner superclass run self.obs[:] = env.reset() on init + actions, values, encoding_indices, self.states, neglogpacs = self.model.step( + self.obs, S=self.states, M=self.dones) + mb_obs.append(self.obs.copy()) + mb_actions.append(actions) + mb_values.append(values) + encoding_indices = np.squeeze(encoding_indices) + + mb_encoding_indices.append(encoding_indices) + mb_neglogpacs.append(neglogpacs) + mb_dones.append(self.dones) + + # Take actions in env and look the results + # Infos contains a ton of useful informations + self.obs[:], rewards, self.dones, infos = self.env.step(actions) + for info in infos: + maybeepinfo = info.get('episode') + if maybeepinfo: epinfos.append(maybeepinfo) + mb_rewards.append(rewards) + # batch of steps to batch of rollouts + mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype) + mb_rewards = np.asarray(mb_rewards, dtype=np.float32) + mb_actions = np.asarray(mb_actions) + mb_values = np.asarray(mb_values, dtype=np.float32) + + mb_encoding_indices = np.asarray(mb_encoding_indices, dtype=np.float32) + mb_neglogpacs = np.asarray(mb_neglogpacs, dtype=np.float32) + mb_dones = np.asarray(mb_dones, dtype=np.bool) + last_values = self.model.value(self.obs, S=self.states, M=self.dones) + + # discount/bootstrap off value fn + mb_returns = np.zeros_like(mb_rewards) + mb_advs = np.zeros_like(mb_rewards) + lastgaelam = 0 + for t in reversed(range(self.nsteps)): + if t == self.nsteps - 1: + nextnonterminal = 1.0 - self.dones + nextvalues = last_values + else: + nextnonterminal = 1.0 - mb_dones[t + 1] + nextvalues = mb_values[t + 1] + delta = mb_rewards[t] + self.gamma * nextvalues * nextnonterminal - mb_values[t] + mb_advs[t] = lastgaelam = delta + self.gamma * self.lam * nextnonterminal * lastgaelam + mb_returns = mb_advs + mb_values + return (*map(sf01, ( + mb_obs, mb_returns, mb_encoding_indices, mb_dones, mb_actions, + mb_values, + mb_neglogpacs)), + mb_states, epinfos) + + +# obs, returns, masks, actions, values, neglogpacs, states = runner.run() +def sf01(arr): + """ + swap and then flatten axes 0 and 1 + """ + s = arr.shape + try: + return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:]) + except: + print() diff --git a/baselines/ppo2/test_microbatches.py b/baselines/ppo2/test_microbatches.py new file mode 100644 index 0000000000000000000000000000000000000000..d740ca89dc46262ee8d4ed25e08efc063e16a8b8 --- /dev/null +++ b/baselines/ppo2/test_microbatches.py @@ -0,0 +1,35 @@ +import gym +import tensorflow as tf +import numpy as np +from functools import partial + +from baselines.common.vec_env.dummy_vec_env import DummyVecEnv +from baselines.common.tf_util import make_session +from baselines.ppo2.ppo2 import learn + +from baselines.ppo2.microbatched_model import MicrobatchedModel + +def test_microbatches(): + def env_fn(): + env = gym.make('CartPole-v0') + env.seed(0) + return env + + learn_fn = partial(learn, network='mlp', nsteps=32, total_timesteps=32, seed=0) + + env_ref = DummyVecEnv([env_fn]) + sess_ref = make_session(make_default=True, graph=tf.Graph()) + learn_fn(env=env_ref) + vars_ref = {v.name: sess_ref.run(v) for v in tf.compat.v1.trainable_variables()} + + env_test = DummyVecEnv([env_fn]) + sess_test = make_session(make_default=True, graph=tf.Graph()) + learn_fn(env=env_test, model_fn=partial(MicrobatchedModel, microbatch_size=2)) + # learn_fn(env=env_test) + vars_test = {v.name: sess_test.run(v) for v in tf.compat.v1.trainable_variables()} + + for v in vars_ref: + np.testing.assert_allclose(vars_ref[v], vars_test[v], atol=3e-3) + +if __name__ == '__main__': + test_microbatches() diff --git a/baselines/results_plotter.py b/baselines/results_plotter.py new file mode 100644 index 0000000000000000000000000000000000000000..66f09bd12d92accb8f989579bb347a19ab0acdd0 --- /dev/null +++ b/baselines/results_plotter.py @@ -0,0 +1,94 @@ +import numpy as np +import matplotlib +matplotlib.use('TkAgg') # Can change to 'Agg' for non-interactive mode + +import matplotlib.pyplot as plt +plt.rcParams['svg.fonttype'] = 'none' + +from baselines.common import plot_util + +X_TIMESTEPS = 'timesteps' +X_EPISODES = 'episodes' +X_WALLTIME = 'walltime_hrs' +Y_REWARD = 'reward' +Y_TIMESTEPS = 'timesteps' +POSSIBLE_X_AXES = [X_TIMESTEPS, X_EPISODES, X_WALLTIME] +EPISODES_WINDOW = 100 +COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink', + 'brown', 'orange', 'teal', 'coral', 'lightblue', 'lime', 'lavender', 'turquoise', + 'darkgreen', 'tan', 'salmon', 'gold', 'darkred', 'darkblue'] + +def rolling_window(a, window): + shape = a.shape[:-1] + (a.shape[-1] - window + 1, window) + strides = a.strides + (a.strides[-1],) + return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) + +def window_func(x, y, window, func): + yw = rolling_window(y, window) + yw_func = func(yw, axis=-1) + return x[window-1:], yw_func + +def ts2xy(ts, xaxis, yaxis): + if xaxis == X_TIMESTEPS: + x = np.cumsum(ts.l.values) + elif xaxis == X_EPISODES: + x = np.arange(len(ts)) + elif xaxis == X_WALLTIME: + x = ts.t.values / 3600. + else: + raise NotImplementedError + if yaxis == Y_REWARD: + y = ts.r.values + elif yaxis == Y_TIMESTEPS: + y = ts.l.values + else: + raise NotImplementedError + return x, y + +def plot_curves(xy_list, xaxis, yaxis, title): + fig = plt.figure(figsize=(8,2)) + maxx = max(xy[0][-1] for xy in xy_list) + minx = 0 + for (i, (x, y)) in enumerate(xy_list): + color = COLORS[i % len(COLORS)] + plt.scatter(x, y, s=2) + x, y_mean = window_func(x, y, EPISODES_WINDOW, np.mean) #So returns average of last EPISODE_WINDOW episodes + plt.plot(x, y_mean, color=color) + plt.xlim(minx, maxx) + plt.title(title) + plt.xlabel(xaxis) + plt.ylabel(yaxis) + plt.tight_layout() + fig.canvas.mpl_connect('resize_event', lambda event: plt.tight_layout()) + plt.grid(True) + + +def split_by_task(taskpath): + return taskpath['dirname'].split('/')[-1].split('-')[0] + +def plot_results(dirs, num_timesteps=10e6, xaxis=X_TIMESTEPS, yaxis=Y_REWARD, title='', split_fn=split_by_task): + results = plot_util.load_results(dirs) + plot_util.plot_results(results, xy_fn=lambda r: ts2xy(r['monitor'], xaxis, yaxis), split_fn=split_fn, average_group=True, resample=int(1e6)) + +# Example usage in jupyter-notebook +# from baselines.results_plotter import plot_results +# %matplotlib inline +# plot_results("./log") +# Here ./log is a directory containing the monitor.csv files + +def main(): + import argparse + import os + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('--dirs', help='List of log directories', nargs = '*', default=['./log']) + parser.add_argument('--num_timesteps', type=int, default=int(10e6)) + parser.add_argument('--xaxis', help = 'Varible on X-axis', default = X_TIMESTEPS) + parser.add_argument('--yaxis', help = 'Varible on Y-axis', default = Y_REWARD) + parser.add_argument('--task_name', help = 'Title of plot', default = 'Breakout') + args = parser.parse_args() + args.dirs = [os.path.abspath(dir) for dir in args.dirs] + plot_results(args.dirs, args.num_timesteps, args.xaxis, args.yaxis, args.task_name) + plt.show() + +if __name__ == '__main__': + main() diff --git a/baselines/run.py b/baselines/run.py new file mode 100644 index 0000000000000000000000000000000000000000..1b285c6ba941f4e23b6b134757d7e9383497734d --- /dev/null +++ b/baselines/run.py @@ -0,0 +1,250 @@ +import sys +import re +import multiprocessing +import os.path as osp +import gym +from collections import defaultdict +import tensorflow as tf +import numpy as np + +from baselines.common.vec_env import VecFrameStack, VecNormalize, VecEnv +from baselines.common.vec_env.vec_video_recorder import VecVideoRecorder +from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_vec_env, make_env +from baselines.common.tf_util import get_session +from baselines import logger +from importlib import import_module + +try: + from mpi4py import MPI +except ImportError: + MPI = None + +try: + import pybullet_envs +except ImportError: + pybullet_envs = None + +try: + import roboschool +except ImportError: + roboschool = None + +_game_envs = defaultdict(set) +for env in gym.envs.registry.all(): + # TODO: solve this with regexes + env_type = env.entry_point.split(':')[0].split('.')[-1] + _game_envs[env_type].add(env.id) + +# reading benchmark names directly from retro requires +# importing retro here, and for some reason that crashes tensorflow +# in ubuntu +_game_envs['retro'] = { + 'BubbleBobble-Nes', + 'SuperMarioBros-Nes', + 'TwinBee3PokoPokoDaimaou-Nes', + 'SpaceHarrier-Nes', + 'SonicTheHedgehog-Genesis', + 'Vectorman-Genesis', + 'FinalFight-Snes', + 'SpaceInvaders-Snes', +} + + +def train(args, extra_args): + env_type, env_id = get_env_type(args) + print('env_type: {}'.format(env_type)) + + total_timesteps = int(args.num_timesteps) + seed = args.seed + + learn = get_learn_function(args.alg) + alg_kwargs = get_learn_function_defaults(args.alg, env_type) + alg_kwargs.update(extra_args) + + env = build_env(args) + if args.save_video_interval != 0: + env = VecVideoRecorder(env, osp.join(logger.get_dir(), "videos"), record_video_trigger=lambda x: x % args.save_video_interval == 0, video_length=args.save_video_length) + + if args.network: + alg_kwargs['network'] = args.network + else: + if alg_kwargs.get('network') is None: + alg_kwargs['network'] = get_default_network(env_type) + + print('Training {} on {}:{} with arguments \n{}'.format(args.alg, env_type, env_id, alg_kwargs)) + + model = learn( + env=env, + seed=seed, + total_timesteps=total_timesteps, + **alg_kwargs + ) + + return model, env + + +def build_env(args): + ncpu = multiprocessing.cpu_count() + if sys.platform == 'darwin': ncpu //= 2 + nenv = args.num_env or ncpu + alg = args.alg + seed = args.seed + + env_type, env_id = get_env_type(args) + + if env_type in {'atari', 'retro'}: + if alg == 'deepq': + env = make_env(env_id, env_type, seed=seed, wrapper_kwargs={'frame_stack': True}) + elif alg == 'trpo_mpi': + env = make_env(env_id, env_type, seed=seed) + else: + frame_stack_size = 4 + env = make_vec_env(env_id, env_type, nenv, seed, gamestate=args.gamestate, reward_scale=args.reward_scale) + env = VecFrameStack(env, frame_stack_size) + + else: + config = tf.compat.v1.ConfigProto(allow_soft_placement=True, + intra_op_parallelism_threads=1, + inter_op_parallelism_threads=1) + config.gpu_options.allow_growth = True + get_session(config=config) + + flatten_dict_observations = alg not in {'her'} + env = make_vec_env(env_id, env_type, args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations) + + if env_type == 'mujoco': + env = VecNormalize(env, use_tf=True) + + return env + + +def get_env_type(args): + env_id = args.env + + if args.env_type is not None: + return args.env_type, env_id + + # Re-parse the gym registry, since we could have new envs since last time. + for env in gym.envs.registry.all(): + env_type = env.entry_point.split(':')[0].split('.')[-1] + _game_envs[env_type].add(env.id) # This is a set so add is idempotent + + if env_id in _game_envs.keys(): + env_type = env_id + env_id = [g for g in _game_envs[env_type]][0] + else: + env_type = None + for g, e in _game_envs.items(): + if env_id in e: + env_type = g + break + if ':' in env_id: + env_type = re.sub(r':.*', '', env_id) + assert env_type is not None, 'env_id {} is not recognized in env types'.format(env_id, _game_envs.keys()) + + return env_type, env_id + + +def get_default_network(env_type): + if env_type in {'atari', 'retro'}: + return 'cnn' + else: + return 'mlp' + +def get_alg_module(alg, submodule=None): + submodule = submodule or alg + try: + # first try to import the alg module from baselines + alg_module = import_module('.'.join(['baselines', alg, submodule])) + except ImportError: + # then from rl_algs + alg_module = import_module('.'.join(['rl_' + 'algs', alg, submodule])) + + return alg_module + + +def get_learn_function(alg): + return get_alg_module(alg).learn + + +def get_learn_function_defaults(alg, env_type): + try: + alg_defaults = get_alg_module(alg, 'defaults') + kwargs = getattr(alg_defaults, env_type)() + except (ImportError, AttributeError): + kwargs = {} + return kwargs + + + +def parse_cmdline_kwargs(args): + ''' + convert a list of '='-spaced command-line arguments to a dictionary, evaluating python objects when possible + ''' + def parse(v): + + assert isinstance(v, str) + try: + return eval(v) + except (NameError, SyntaxError): + return v + + return {k: parse(v) for k,v in parse_unknown_args(args).items()} + + +def configure_logger(log_path, **kwargs): + if log_path is not None: + logger.configure(log_path) + else: + logger.configure(**kwargs) + + +def main(args): + # configure logger, disable logging in child MPI processes (with rank > 0) + + arg_parser = common_arg_parser() + args, unknown_args = arg_parser.parse_known_args(args) + extra_args = parse_cmdline_kwargs(unknown_args) + + if MPI is None or MPI.COMM_WORLD.Get_rank() == 0: + rank = 0 + configure_logger(args.log_path) + else: + rank = MPI.COMM_WORLD.Get_rank() + configure_logger(args.log_path, format_strs=[]) + + model, env = train(args, extra_args) + + if args.save_path is not None and rank == 0: + save_path = osp.expanduser(args.save_path) + model.save(save_path) + + if args.play: + logger.log("Running trained model") + obs = env.reset() + + state = model.initial_state if hasattr(model, 'initial_state') else None + dones = np.zeros((1,)) + + episode_rew = np.zeros(env.num_envs) if isinstance(env, VecEnv) else np.zeros(1) + while True: + if state is not None: + actions, _, state, _ = model.step(obs,S=state, M=dones) + else: + actions, _, _, _ = model.step(obs) + + obs, rew, done, _ = env.step(actions) + episode_rew += rew + env.render() + done_any = done.any() if isinstance(done, np.ndarray) else done + if done_any: + for i in np.nonzero(done)[0]: + print('episode_rew={}'.format(episode_rew[i])) + episode_rew[i] = 0 + + env.close() + + return model + +if __name__ == '__main__': + main(sys.argv) diff --git a/baselines/trpo_mpi/README.md b/baselines/trpo_mpi/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4cdbb5aee2d09cb4d38e0d237fbdac6576e529bf --- /dev/null +++ b/baselines/trpo_mpi/README.md @@ -0,0 +1,7 @@ +# trpo_mpi + +- Original paper: https://arxiv.org/abs/1502.05477 +- Baselines blog post https://blog.openai.com/openai-baselines-ppo/ +- `mpirun -np 16 python -m baselines.run --alg=trpo_mpi --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options. +- `python -m baselines.run --alg=trpo_mpi --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M timesteps on a Mujoco Ant environment. +- also refer to the repo-wide [README.md](../../README.md#training-models) diff --git a/baselines/trpo_mpi/__init__.py b/baselines/trpo_mpi/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/baselines/trpo_mpi/defaults.py b/baselines/trpo_mpi/defaults.py new file mode 100644 index 0000000000000000000000000000000000000000..2ac3f7af35eb4c39eb4a3ca64438189781bd63eb --- /dev/null +++ b/baselines/trpo_mpi/defaults.py @@ -0,0 +1,30 @@ +from baselines.common.models import mlp, cnn_small + + +def atari(): + return dict( + network = cnn_small(), + timesteps_per_batch=512, + max_kl=0.001, + cg_iters=10, + cg_damping=1e-3, + gamma=0.98, + lam=1.0, + vf_iters=3, + vf_stepsize=1e-4, + entcoeff=0.00, + ) + +def mujoco(): + return dict( + network = mlp(num_hidden=32, num_layers=2), + timesteps_per_batch=1024, + max_kl=0.01, + cg_iters=10, + cg_damping=0.1, + gamma=0.99, + lam=0.98, + vf_iters=5, + vf_stepsize=1e-3, + normalize_observations=True, + ) diff --git a/baselines/trpo_mpi/trpo_mpi.py b/baselines/trpo_mpi/trpo_mpi.py new file mode 100644 index 0000000000000000000000000000000000000000..8586adbf851c76f6048dd92872493623a9129996 --- /dev/null +++ b/baselines/trpo_mpi/trpo_mpi.py @@ -0,0 +1,408 @@ +from baselines.common import explained_variance, zipsame, dataset +from baselines import logger +import baselines.common.tf_util as U +import tensorflow as tf, numpy as np +import time +from baselines.common import colorize +from collections import deque +from baselines.common import set_global_seeds +from baselines.common.mpi_adam import MpiAdam +from baselines.common.cg import cg +from baselines.common.input import observation_placeholder +from baselines.common.policies import build_policy +from contextlib import contextmanager + +try: + from mpi4py import MPI +except ImportError: + MPI = None + +def traj_segment_generator(pi, env, horizon, stochastic): + # Initialize state variables + t = 0 + ac = env.action_space.sample() + new = True + rew = 0.0 + ob = env.reset() + + cur_ep_ret = 0 + cur_ep_len = 0 + ep_rets = [] + ep_lens = [] + + # Initialize history arrays + obs = np.array([ob for _ in range(horizon)]) + rews = np.zeros(horizon, 'float32') + vpreds = np.zeros(horizon, 'float32') + news = np.zeros(horizon, 'int32') + acs = np.array([ac for _ in range(horizon)]) + prevacs = acs.copy() + + while True: + prevac = ac + ac, vpred, _, _ = pi.step(ob, stochastic=stochastic) + # Slight weirdness here because we need value function at time T + # before returning segment [0, T-1] so we get the correct + # terminal value + if t > 0 and t % horizon == 0: + yield {"ob" : obs, "rew" : rews, "vpred" : vpreds, "new" : news, + "ac" : acs, "prevac" : prevacs, "nextvpred": vpred * (1 - new), + "ep_rets" : ep_rets, "ep_lens" : ep_lens} + _, vpred, _, _ = pi.step(ob, stochastic=stochastic) + # Be careful!!! if you change the downstream algorithm to aggregate + # several of these batches, then be sure to do a deepcopy + ep_rets = [] + ep_lens = [] + i = t % horizon + obs[i] = ob + vpreds[i] = vpred + news[i] = new + acs[i] = ac + prevacs[i] = prevac + + ob, rew, new, _ = env.step(ac) + rews[i] = rew + + cur_ep_ret += rew + cur_ep_len += 1 + if new: + ep_rets.append(cur_ep_ret) + ep_lens.append(cur_ep_len) + cur_ep_ret = 0 + cur_ep_len = 0 + ob = env.reset() + t += 1 + +def add_vtarg_and_adv(seg, gamma, lam): + new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1 + vpred = np.append(seg["vpred"], seg["nextvpred"]) + T = len(seg["rew"]) + seg["adv"] = gaelam = np.empty(T, 'float32') + rew = seg["rew"] + lastgaelam = 0 + for t in reversed(range(T)): + nonterminal = 1-new[t+1] + delta = rew[t] + gamma * vpred[t+1] * nonterminal - vpred[t] + gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam + seg["tdlamret"] = seg["adv"] + seg["vpred"] + +def learn(*, + network, + env, + total_timesteps, + timesteps_per_batch=1024, # what to train on + max_kl=0.001, + cg_iters=10, + gamma=0.99, + lam=1.0, # advantage estimation + seed=None, + ent_coef=0.0, + cg_damping=1e-2, + vf_stepsize=3e-4, + vf_iters =3, + max_episodes=0, max_iters=0, # time constraint + callback=None, + load_path=None, + **network_kwargs + ): + ''' + learn a policy function with TRPO algorithm + + Parameters: + ---------- + + network neural network to learn. Can be either string ('mlp', 'cnn', 'lstm', 'lnlstm' for basic types) + or function that takes input placeholder and returns tuple (output, None) for feedforward nets + or (output, (state_placeholder, state_output, mask_placeholder)) for recurrent nets + + env environment (one of the gym environments or wrapped via baselines.common.vec_env.VecEnv-type class + + timesteps_per_batch timesteps per gradient estimation batch + + max_kl max KL divergence between old policy and new policy ( KL(pi_old || pi) ) + + ent_coef coefficient of policy entropy term in the optimization objective + + cg_iters number of iterations of conjugate gradient algorithm + + cg_damping conjugate gradient damping + + vf_stepsize learning rate for adam optimizer used to optimie value function loss + + vf_iters number of iterations of value function optimization iterations per each policy optimization step + + total_timesteps max number of timesteps + + max_episodes max number of episodes + + max_iters maximum number of policy optimization iterations + + callback function to be called with (locals(), globals()) each policy optimization step + + load_path str, path to load the model from (default: None, i.e. no model is loaded) + + **network_kwargs keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network + + Returns: + ------- + + learnt model + + ''' + + if MPI is not None: + nworkers = MPI.COMM_WORLD.Get_size() + rank = MPI.COMM_WORLD.Get_rank() + else: + nworkers = 1 + rank = 0 + + cpus_per_worker = 1 + U.get_session(config=tf.compat.v1.ConfigProto( + allow_soft_placement=True, + inter_op_parallelism_threads=cpus_per_worker, + intra_op_parallelism_threads=cpus_per_worker + )) + + + policy = build_policy(env, network, value_network='copy', **network_kwargs) + set_global_seeds(seed) + + np.set_printoptions(precision=3) + # Setup losses and stuff + # ---------------------------------------- + ob_space = env.observation_space + ac_space = env.action_space + + ob = observation_placeholder(ob_space) + with tf.compat.v1.variable_scope("pi"): + pi = policy(observ_placeholder=ob) + with tf.compat.v1.variable_scope("oldpi"): + oldpi = policy(observ_placeholder=ob) + + atarg = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable) + ret = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None]) # Empirical return + + ac = pi.pdtype.sample_placeholder([None]) + + kloldnew = oldpi.pd.kl(pi.pd) + ent = pi.pd.entropy() + meankl = tf.reduce_mean(input_tensor=kloldnew) + meanent = tf.reduce_mean(input_tensor=ent) + entbonus = ent_coef * meanent + + vferr = tf.reduce_mean(input_tensor=tf.square(pi.vf - ret)) + + ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # advantage * pnew / pold + surrgain = tf.reduce_mean(input_tensor=ratio * atarg) + + optimgain = surrgain + entbonus + losses = [optimgain, meankl, entbonus, surrgain, meanent] + loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"] + + dist = meankl + + all_var_list = get_trainable_variables("pi") + # var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")] + # vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")] + var_list = get_pi_trainable_variables("pi") + vf_var_list = get_vf_trainable_variables("pi") + + vfadam = MpiAdam(vf_var_list) + + get_flat = U.GetFlat(var_list) + set_from_flat = U.SetFromFlat(var_list) + klgrads = tf.gradients(ys=dist, xs=var_list) + flat_tangent = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None], name="flat_tan") + shapes = [var.get_shape().as_list() for var in var_list] + start = 0 + tangents = [] + for shape in shapes: + sz = U.intprod(shape) + tangents.append(tf.reshape(flat_tangent[start:start+sz], shape)) + start += sz + gvp = tf.add_n([tf.reduce_sum(input_tensor=g*tangent) for (g, tangent) in zipsame(klgrads, tangents)]) #pylint: disable=E1111 + fvp = U.flatgrad(gvp, var_list) + + assign_old_eq_new = U.function([],[], updates=[tf.compat.v1.assign(oldv, newv) + for (oldv, newv) in zipsame(get_variables("oldpi"), get_variables("pi"))]) + + compute_losses = U.function([ob, ac, atarg], losses) + compute_lossandgrad = U.function([ob, ac, atarg], losses + [U.flatgrad(optimgain, var_list)]) + compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp) + compute_vflossandgrad = U.function([ob, ret], U.flatgrad(vferr, vf_var_list)) + + @contextmanager + def timed(msg): + if rank == 0: + print(colorize(msg, color='magenta')) + tstart = time.time() + yield + print(colorize("done in %.3f seconds"%(time.time() - tstart), color='magenta')) + else: + yield + + def allmean(x): + assert isinstance(x, np.ndarray) + if MPI is not None: + out = np.empty_like(x) + MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM) + out /= nworkers + else: + out = np.copy(x) + + return out + + U.initialize() + if load_path is not None: + pi.load(load_path) + + th_init = get_flat() + if MPI is not None: + MPI.COMM_WORLD.Bcast(th_init, root=0) + + set_from_flat(th_init) + vfadam.sync() + print("Init param sum", th_init.sum(), flush=True) + + # Prepare for rollouts + # ---------------------------------------- + seg_gen = traj_segment_generator(pi, env, timesteps_per_batch, stochastic=True) + + episodes_so_far = 0 + timesteps_so_far = 0 + iters_so_far = 0 + tstart = time.time() + lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths + rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards + + if sum([max_iters>0, total_timesteps>0, max_episodes>0])==0: + # noththing to be done + return pi + + assert sum([max_iters>0, total_timesteps>0, max_episodes>0]) < 2, \ + 'out of max_iters, total_timesteps, and max_episodes only one should be specified' + + while True: + if callback: callback(locals(), globals()) + if total_timesteps and timesteps_so_far >= total_timesteps: + break + elif max_episodes and episodes_so_far >= max_episodes: + break + elif max_iters and iters_so_far >= max_iters: + break + logger.log("********** Iteration %i ************"%iters_so_far) + + with timed("sampling"): + seg = seg_gen.__next__() + add_vtarg_and_adv(seg, gamma, lam) + + # ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets)) + ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"] + vpredbefore = seg["vpred"] # predicted value function before udpate + atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate + + if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret) + if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy + + args = seg["ob"], seg["ac"], atarg + fvpargs = [arr[::5] for arr in args] + def fisher_vector_product(p): + return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p + + assign_old_eq_new() # set old parameter values to new parameter values + with timed("computegrad"): + *lossbefore, g = compute_lossandgrad(*args) + lossbefore = allmean(np.array(lossbefore)) + g = allmean(g) + if np.allclose(g, 0): + logger.log("Got zero gradient. not updating") + else: + with timed("cg"): + stepdir = cg(fisher_vector_product, g, cg_iters=cg_iters, verbose=rank==0) + assert np.isfinite(stepdir).all() + shs = .5*stepdir.dot(fisher_vector_product(stepdir)) + lm = np.sqrt(shs / max_kl) + # logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g)) + fullstep = stepdir / lm + expectedimprove = g.dot(fullstep) + surrbefore = lossbefore[0] + stepsize = 1.0 + thbefore = get_flat() + for _ in range(10): + thnew = thbefore + fullstep * stepsize + set_from_flat(thnew) + meanlosses = surr, kl, *_ = allmean(np.array(compute_losses(*args))) + improve = surr - surrbefore + logger.log("Expected: %.3f Actual: %.3f"%(expectedimprove, improve)) + if not np.isfinite(meanlosses).all(): + logger.log("Got non-finite value of losses -- bad!") + elif kl > max_kl * 1.5: + logger.log("violated KL constraint. shrinking step.") + elif improve < 0: + logger.log("surrogate didn't improve. shrinking step.") + else: + logger.log("Stepsize OK!") + break + stepsize *= .5 + else: + logger.log("couldn't compute a good step") + set_from_flat(thbefore) + if nworkers > 1 and iters_so_far % 20 == 0: + paramsums = MPI.COMM_WORLD.allgather((thnew.sum(), vfadam.getflat().sum())) # list of tuples + assert all(np.allclose(ps, paramsums[0]) for ps in paramsums[1:]) + + for (lossname, lossval) in zip(loss_names, meanlosses): + logger.record_tabular(lossname, lossval) + + with timed("vf"): + + for _ in range(vf_iters): + for (mbob, mbret) in dataset.iterbatches((seg["ob"], seg["tdlamret"]), + include_final_partial_batch=False, batch_size=64): + g = allmean(compute_vflossandgrad(mbob, mbret)) + vfadam.update(g, vf_stepsize) + + logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret)) + + lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values + if MPI is not None: + listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples + else: + listoflrpairs = [lrlocal] + + lens, rews = map(flatten_lists, zip(*listoflrpairs)) + lenbuffer.extend(lens) + rewbuffer.extend(rews) + + logger.record_tabular("EpLenMean", np.mean(lenbuffer)) + logger.record_tabular("EpRewMean", np.mean(rewbuffer)) + logger.record_tabular("EpThisIter", len(lens)) + episodes_so_far += len(lens) + timesteps_so_far += sum(lens) + iters_so_far += 1 + + logger.record_tabular("EpisodesSoFar", episodes_so_far) + logger.record_tabular("TimestepsSoFar", timesteps_so_far) + logger.record_tabular("TimeElapsed", time.time() - tstart) + + if rank==0: + logger.dump_tabular() + + return pi + +def flatten_lists(listoflists): + return [el for list_ in listoflists for el in list_] + +def get_variables(scope): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope) + +def get_trainable_variables(scope): + return tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, scope) + +def get_vf_trainable_variables(scope): + return [v for v in get_trainable_variables(scope) if 'vf' in v.name[len(scope):].split('/')] + +def get_pi_trainable_variables(scope): + return [v for v in get_trainable_variables(scope) if 'pi' in v.name[len(scope):].split('/')] + diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..7d182d61200feb693ca926a0551161ddfccf11fc --- /dev/null +++ b/environment.yml @@ -0,0 +1,12 @@ +channels: + - conda-forge + +dependencies: + - python=3.7.3 + - mpi4py=3.0.3 + - pip + - pip: + - gym==0.15.4 + - procgen==0.9.2 + - tensorflow==1.15.0 + - pytest # for baselines \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..1885a87f3c438ad04a4a0ef9276f6d285fa48a4b --- /dev/null +++ b/requirements.txt @@ -0,0 +1,127 @@ +absl-py==0.15.0 +appnope==0.1.4 +asttokens==3.0.0 +astunparse==1.6.3 +attrs==25.3.0 +backcall==0.2.0 +bleach==6.1.0 +cachetools==4.2.4 +certifi==2021.10.8 +cffi==1.15.0 +charset-normalizer==2.0.12 +cloudpickle==1.2.2 +cmake==3.22.2 +contourpy==1.1.1 +cycler==0.11.0 +decorator==5.2.1 +defusedxml==0.7.1 +docopt==0.6.2 +executing==2.2.1 +fastjsonschema==2.21.2 +filelock==3.6.0 +flatbuffers==1.12 +fonttools==4.31.2 +future==0.18.2 +gast==0.3.3 +glcontext==2.3.5 +glfw==1.12.0 +google-auth==1.35.0 +google-auth-oauthlib==0.4.6 +google-pasta==0.2.0 +grpcio==1.32.0 +gym==0.16.0 +gym3==0.3.3 +h5py==2.10.0 +idna==3.3 +imageio==2.9.0 +imageio-ffmpeg==0.3.0 +importlib-metadata==4.11.2 +importlib_resources==6.4.5 +iniconfig==1.1.1 +ipython==8.12.3 +jedi==0.19.2 +Jinja2==3.1.6 +joblib==1.1.0 +jsonschema==4.23.0 +jsonschema-specifications==2023.12.1 +jupyter_client==8.6.3 +jupyter_core==5.8.1 +jupyterlab_pygments==0.3.0 +Keras==2.4.3 +Keras-Preprocessing==1.1.2 +kiwisolver==1.4.2 +libclang==13.0.0 +llvmlite==0.38.0 +Markdown==3.7 +MarkupSafe==2.1.5 +matplotlib==3.5.1 +matplotlib-inline==0.1.7 +mistune==3.1.4 +moderngl==5.6.4 +mpi4py==3.1.3 +nbclient==0.10.1 +nbformat==5.10.4 +numpy==1.19.5 +oauthlib==3.2.0 +opencv-python==4.5.5.64 +opt-einsum==3.3.0 +packaging==21.3 +pandas==1.4.1 +pandocfilters==1.5.1 +parso==0.8.5 +pexpect==4.9.0 +pickleshare==0.7.5 +Pillow==9.0.1 +pipreqs==0.5.0 +pkgutil_resolve_name==1.3.10 +platformdirs==4.3.6 +pluggy==1.0.0 +procgen==0.10.7 +prompt_toolkit==3.0.52 +protobuf==3.19.4 +ptyprocess==0.7.0 +pure_eval==0.2.3 +py==1.11.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pycparser==2.21 +pyglet==1.5.0 +Pygments==2.19.2 +pyparsing==3.0.7 +python-dateutil==2.8.2 +pytz==2022.1 +PyYAML==6.0 +pyzmq==27.1.0 +referencing==0.35.1 +requests==2.27.1 +requests-oauthlib==2.0.0 +rpds-py==0.20.1 +rsa==4.8 +scikit-learn==1.0.2 +scipy==1.8.0 +seaborn==0.11.2 +six==1.15.0 +soupsieve==2.7 +stack-data==0.6.3 +tensorboard==2.4.1 +tensorboard-data-server==0.6.1 +tensorboard-plugin-wit==1.8.1 +tensorflow==2.4.4 +tensorflow-estimator==2.4.0 +tensorflow-io-gcs-filesystem==0.23.1 +termcolor==1.1.0 +tf-estimator-nightly==2.8.0.dev2021122109 +threadpoolctl==3.1.0 +tinycss2==1.2.1 +tomli==2.0.1 +tornado==6.4.2 +traitlets==5.14.3 +typing-extensions==3.7.4.3 +tzdata==2025.2 +urllib3==1.26.8 +wcwidth==0.2.13 +webencodings==0.5.1 +Werkzeug==2.0.3 +wrapt==1.12.1 +yarg==0.1.9 +zipp==3.7.0 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..19e33ff956a58130f85970461208109c1e2ff7b4 --- /dev/null +++ b/setup.py @@ -0,0 +1,7 @@ +from setuptools import setup, find_packages + +setup( + name='train-procgen', + packages=find_packages(), + version='0.0.1', +) diff --git a/train_procgen/__init__.py b/train_procgen/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..452484e74ca55b1f93b6d3c12c262c59a63c956e --- /dev/null +++ b/train_procgen/__init__.py @@ -0,0 +1 @@ +# from .train_ppo import train_fn \ No newline at end of file diff --git a/train_procgen/checkpoints/ppo-coinrun_easy_0_0_2020/progress.csv b/train_procgen/checkpoints/ppo-coinrun_easy_0_0_2020/progress.csv new file mode 100644 index 0000000000000000000000000000000000000000..c9462148182aed11c28c5765bae3a8cbf568ad91 --- /dev/null +++ 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@@ +ENV_NAMES = [ + "coinrun", + "starpilot", + "caveflyer", + "dodgeball", + "fruitbot", + "chaser", + "miner", + "jumper", + "leaper", + "maze", + "bigfish", + "heist", + "climber", + "plunder", + "ninja", + "bossfight", +] + +HARD_GAME_RANGES = { + 'coinrun': [5, 10], + 'starpilot': [1.5, 35], + 'caveflyer': [2, 13.4], + 'dodgeball': [1.5, 19], + 'fruitbot': [-.5, 27.2], + 'chaser': [.5, 14.2], + 'miner': [1.5, 20], + 'jumper': [1, 10], + 'leaper': [1.5, 10], + 'maze': [4, 10], + 'bigfish': [0, 40], + 'heist': [2, 10], + 'climber': [1, 12.6], + 'plunder': [3, 30], + 'ninja': [2, 10], + 'bossfight': [.5, 13], +} + +EASY_GAME_RANGES = { + 'coinrun': [5, 10], + 'starpilot': [2.5, 64], + 'caveflyer': [3.5, 12], + 'dodgeball': [1.5, 19], + 'fruitbot': [-1.5, 32.4], + 'chaser': [.5, 13], + 'miner': [1.5, 13], + 'jumper': [1, 10], + 'leaper': [1.5, 10], + 'maze': [5, 10], + 'bigfish': [1, 40], + 'heist': [3.5, 10], + 'climber': [2, 12.6], + 'plunder': [4.5, 30], + 'ninja': [3.5, 10], + 'bossfight': [.5, 13], +} + +NAME_TO_CASE = { + 'coinrun': 'CoinRun', + 'starpilot': 'StarPilot', + 'caveflyer': 'CaveFlyer', + 'dodgeball': 'Dodgeball', + 'fruitbot': 'FruitBot', + 'chaser': 'Chaser', + 'miner': 'Miner', + 'jumper': 'Jumper', + 'leaper': 'Leaper', + 'maze': 'Maze', + 'bigfish': 'BigFish', + 'heist': 'Heist', + 'climber': 'Climber', + 'plunder': 'Plunder', + 'ninja': 'Ninja', + 'bossfight': 'BossFight', +} \ No newline at end of file diff --git a/train_procgen/enjoy_sppo.py b/train_procgen/enjoy_sppo.py new file mode 100644 index 0000000000000000000000000000000000000000..ebade63d306f61b7fca3fd0377a68bd3696ff122 --- /dev/null +++ b/train_procgen/enjoy_sppo.py @@ -0,0 +1,349 @@ +import math +import os +import sys +import shutil + +if not sys.warnoptions: + import warnings + + warnings.simplefilter("ignore") + +with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=FutureWarning) + warnings.filterwarnings("ignore", category=RuntimeWarning) + warnings.simplefilter("ignore") + import tensorflow as tf + +tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +tf.get_logger().setLevel('INFO') +tf.autograph.set_verbosity(0) +import warnings + +warnings.filterwarnings("ignore", message=r"Passing", category=FutureWarning) +warnings.simplefilter(action='ignore', category=FutureWarning) +warnings.simplefilter(action='ignore', category=Warning) + +from baselines.ppo2 import sppo2 +from baselines.common.models import build_skill_impala_cnn +from baselines.common.mpi_util import setup_mpi_gpus +from procgen import ProcgenEnv +from baselines.common.vec_env import ( + VecExtractDictObs, + VecMonitor, + VecNormalize +) +from baselines import logger +from mpi4py import MPI +import argparse +import matplotlib.colors as c +import matplotlib.patches as mpatches +import gym +from sklearn.manifold import TSNE +from PIL import Image +import matplotlib +import matplotlib.pyplot as plt +from baselines.common import set_global_seeds +from train_procgen.utils import * +from train_procgen.constants import EASY_GAME_RANGES +from copy import deepcopy + +parser = argparse.ArgumentParser(description='Process procgen enjoying arguments.') +parser.add_argument('--env_name', type=str, default='coinrun') +parser.add_argument('--num_envs', type=int, default=64) +parser.add_argument('--distribution_mode', type=str, default='easy', + choices=["easy", "hard", "exploration", "memory", "extreme"]) +parser.add_argument('--num_levels', type=int, default=0) +parser.add_argument('--start_level', type=int, default=0) +parser.add_argument('--test_worker_interval', type=int, default=0) +parser.add_argument('--timesteps_per_proc', type=int, default=25_000_000) +parser.add_argument('--rand_seed', type=int, default=2021) +parser.add_argument('--num_embeddings', type=int, default=8) +parser.add_argument('--beta', type=float, default=0.0) +parser.add_argument('--alpha1', type=float, default=20) +parser.add_argument('--alpha2', type=float, default=20) +parser.add_argument('--mode', type=int, default=1, + help='0: generate clustering videos 1: generate embedding spaces') + +args = parser.parse_args() + + +def load_model(env_name, num_envs, distribution_mode, num_levels, start_level, + num_embeddings, beta, alpha1, alpha2, + log_dir='./checkpoints', comm=None, rand_seed=None): + ent_coef = .01 + nsteps = 256 + nminibatches = 8 + + mpi_rank_weight = 1 + num_levels = num_levels + + logger.info("creating environment") + venv = ProcgenEnv(num_envs=num_envs, env_name=env_name, num_levels=num_levels, start_level=start_level + 200000, + distribution_mode=distribution_mode, rand_seed=rand_seed) + venv = VecExtractDictObs(venv, "rgb") + + venv = VecMonitor( + venv=venv, filename=None, keep_buf=100, + ) + + venv = VecNormalize(venv=venv, ob=False) + + logger.info("creating tf session") + setup_mpi_gpus() + config = tf.compat.v1.ConfigProto() + config.gpu_options.allow_growth = True # pylint: disable=E1101 + sess = tf.compat.v1.Session(config=config) + sess.__enter__() + + conv_fn = lambda x: build_skill_impala_cnn(x, depths=[16, 32, 32], emb_dim=256, num_embeddings=num_embeddings, + beta=beta, seed=rand_seed) + + logger.info("loading the model") + + model = sppo2.load( + env=venv, + network=conv_fn, + nsteps=nsteps, + nminibatches=nminibatches, + ent_coef=ent_coef, + mpi_rank_weight=mpi_rank_weight, + comm=comm, + vf_coef=0.5, + max_grad_norm=0.5, + seed=rand_seed, + load_path=log_dir, + alpha1=alpha1, + alpha2=alpha2, + ) + + return model + + +def main(): + plt.rcParams.update({'font.size': 15}) + comm = MPI.COMM_WORLD + + saved_dir = './train_procgen/checkpoints/sppo-' + args.env_name + '_' + args.distribution_mode + '_' + str( + args.num_levels) + '_' + str(args.start_level) + '_' + str( + args.rand_seed) + + saved_dir += '/checkpoints/' + str(args.timesteps_per_proc) + + if not os.path.isdir('./train_procgen/figures'): + os.mkdir('./train_procgen/figures') + + if args.mode == 0: + model = load_model(args.env_name, + 1, + args.distribution_mode, + args.num_levels, + args.start_level, + args.num_embeddings, + comm=comm, + log_dir=saved_dir, + rand_seed=args.rand_seed, + beta=args.beta, + alpha1=args.alpha1, + alpha2=args.alpha2, + ) + + cluster_path = './train_procgen/figures/' + args.env_name + '_skills' + + if not os.path.isdir(cluster_path): + os.mkdir(cluster_path) + for i in range(args.num_embeddings): + if not os.path.isdir(cluster_path + '/cluster_' + str(i)): + os.mkdir(cluster_path + '/cluster_' + str(i)) + + # generate the skill clusters + set_global_seeds(args.rand_seed) + env_name = "procgen:procgen-" + args.env_name + "-v0" + env = gym.make(env_name, num_levels=args.num_levels, start_level=args.start_level, + distribution_mode=args.distribution_mode, rand_seed=args.rand_seed, render_mode="human") + obs = env.reset() + counters = np.zeros(args.num_embeddings) + + episode_i = 0 + total_frames = 480 + episode_files = [] + sequence_files = [[] for _ in range(args.num_embeddings)] + sequence_indices = [[] for _ in range(args.num_embeddings)] + episode_rewards = 0 + episode_counters = np.zeros(args.num_embeddings) + # avoid to collect the endless loop + if args.env_name not in ['starpilot', 'fruitbot']: + episode_max_steps = 200 + else: + episode_max_steps = math.inf + + while True: + rgb_img = env.render(mode="rgb_array") + im = Image.fromarray(rgb_img) + a, v, pure_latent, vq_latent, pure_vq_latent, vq_embeddings, encoding_indices, sl, lat = model.skill_step( + obs) + action = np.squeeze(a) + if episode_counters[encoding_indices[0][0]] < total_frames: + saved_dir = cluster_path + '/cluster_' + str(encoding_indices[0][0]) + file_name = str(episode_i + 1) + '_' + str(action) + '.png' + im.save(saved_dir + '/' + file_name, dpi=(30, 30)) + episode_files.append(saved_dir + '/' + file_name) + if len(sequence_files[encoding_indices[0][0]]) == 0: + sequence_files[encoding_indices[0][0]].append(saved_dir + '/' + file_name) + sequence_indices[encoding_indices[0][0]].append(episode_i + 1) + else: + if (episode_i + 1 - int(sequence_indices[encoding_indices[0][0]][-1])) == 1: + sequence_files[encoding_indices[0][0]].append(saved_dir + '/' + file_name) + sequence_indices[encoding_indices[0][0]].append(episode_i + 1) + else: + if len(sequence_files[encoding_indices[0][0]]) <= 4 or len( + sequence_files[encoding_indices[0][0]]) > 20: + for f in sequence_files[encoding_indices[0][0]]: + if os.path.isfile(f): + os.remove(f) + episode_counters[encoding_indices[0][0]] -= 1 + else: + continue + + sequence_files[encoding_indices[0][0]] = [] + sequence_indices[encoding_indices[0][0]] = [] + sequence_files[encoding_indices[0][0]].append(saved_dir + '/' + file_name) + sequence_indices[encoding_indices[0][0]].append(episode_i + 1) + + obs, reward, done, info = env.step(action) + episode_rewards += reward + episode_counters[encoding_indices[0][0]] += 1 + episode_i += 1 + + if done: + if episode_rewards <= EASY_GAME_RANGES[args.env_name][-1] * 0.8 or len( + episode_files) > episode_max_steps: + for f in episode_files: + if os.path.isfile(f): + os.remove(f) + for i in range(args.num_embeddings): + if f in sequence_files[i]: + sequence_indices[i].pop(sequence_files[i].index(f)) + sequence_files[i].remove(f) + else: + continue + + episode_counters = deepcopy(counters) + counters = deepcopy(episode_counters) + episode_files = [] + episode_rewards = 0 + + if np.min(counters) >= total_frames: + break + + for i in range(args.num_embeddings): + convert_frames_to_video(cluster_path + '/cluster_' + str(i) + '/', + cluster_path + '/cluster_' + str(i) + '.avi', limits=total_frames) + shutil.rmtree(cluster_path + '/cluster_' + str(i)) + + if args.mode == 1: + figure_name_prefix = args.env_name + '_' + str(args.num_levels) + '_' + # visualize the embedding spaces of pure features, skill latents, and vq latents + model = load_model(args.env_name, + args.num_envs, + args.distribution_mode, + args.num_levels, + args.start_level, + args.num_embeddings, + comm=comm, + log_dir=saved_dir, + rand_seed=args.rand_seed, + beta=args.beta, + alpha1=args.alpha1, + alpha2=args.alpha2, + ) + + set_global_seeds(args.rand_seed) + + venv = ProcgenEnv(num_envs=args.num_envs, env_name=args.env_name, num_levels=args.num_levels, + start_level=args.start_level + 200000, + distribution_mode=args.distribution_mode, rand_seed=args.rand_seed) + obs = venv.reset() + + pure_features = [] + skill_latents = [] + enc_indices = [] + values = [] + + for i in range(500): + a, v, pure_latent, vq_latent, pure_vq_latent, vq_embeddings, encoding_indices, sl, lat = model.skill_step( + obs['rgb']) + action = np.squeeze(a) + # record 8/10 data to ensure explore more states + if np.random.uniform(0, 1) > 0.2: + pure_features.append(pure_latent) + skill_latents.append(sl) + enc_indices.append(encoding_indices) + values.append(v) + + action = [np.random.choice([np.random.choice([i for i in range(15)]), action[i]], p=[0.2, 0.8]) for i in + range(args.num_envs)] + obs, reward, done, info = venv.step(np.array(action, dtype=np.int32)) + + pure_features = np.reshape(np.squeeze(np.array(pure_features)), (-1, 256)) + skill_latents = np.reshape(np.squeeze(np.array(skill_latents)), (-1, 2)) + enc_indices = np.reshape(np.squeeze(np.array(enc_indices)), (-1)) + values = np.reshape(np.squeeze(np.array(values)), (-1)) + + n_components = 2 + tsne = TSNE(n_components) + tsne_result = tsne.fit_transform(pure_features) + + fig, ax = plt.subplots() + fig.set_figheight(6) + fig.set_figwidth(8) + np.random.seed(0) + color_set = [(np.random.rand(), np.random.rand(), np.random.rand()) for _ in range(args.num_embeddings)] + + c_cet = [] + for cs in color_set: + c_cet.append(c.to_hex(cs)) + c_cet = np.array(c_cet) + plt.scatter(tsne_result[:, 0], tsne_result[:, 1], s=10, c=c_cet[enc_indices], alpha=0.5) + + pop_ = [] + for i in range(args.num_embeddings): + if i in enc_indices: + pop_.append(mpatches.Patch(color=color_set[i], label='Cluster ' + str(i))) + + plt.legend(handles=pop_, bbox_to_anchor=(1.02, 1), loc=2, borderaxespad=0, numpoints=1, fontsize=10) + + plt.title('The t-SNE space') + plt.xlabel('t-SNE x') + plt.ylabel('t-SNE y') + + fig.savefig('./train_procgen/figures/' + figure_name_prefix + 'feature_embeddings.png', dpi=600, bbox_inches='tight') + # plt.show() + + fig, ax = plt.subplots() + fig.set_figheight(6) + fig.set_figwidth(8) + np.random.seed(0) + color_set = [(np.random.rand(), np.random.rand(), np.random.rand()) for _ in range(args.num_embeddings)] + + c_cet = [] + for cs in color_set: + c_cet.append(c.to_hex(cs)) + c_cet = np.array(c_cet) + plt.scatter(skill_latents[:, 0], skill_latents[:, 1], s=10, c=c_cet[enc_indices], alpha=0.5) + + pop_ = [] + for i in range(args.num_embeddings): + if i in enc_indices: + pop_.append(mpatches.Patch(color=color_set[i], label='Cluster ' + str(i))) + + plt.legend(handles=pop_, bbox_to_anchor=(1.02, 1), loc=2, borderaxespad=0, numpoints=1, fontsize=10) + + plt.title('The FDR space') + plt.xlabel('FDR x') + plt.ylabel('FDR y') + + fig.savefig('./train_procgen/figures/' + figure_name_prefix + 'fdr_embeddings.png', dpi=600, bbox_inches='tight') + # plt.show() + +if __name__ == '__main__': + main() diff --git a/train_procgen/hover_clusters.py b/train_procgen/hover_clusters.py new file mode 100644 index 0000000000000000000000000000000000000000..3e29b5d93609fa3a78fe144ba84f924357d69639 --- /dev/null +++ b/train_procgen/hover_clusters.py @@ -0,0 +1,322 @@ +import math +import sys + +if not sys.warnoptions: + import warnings + + warnings.simplefilter("ignore") + +with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=FutureWarning) + warnings.filterwarnings("ignore", category=RuntimeWarning) + warnings.simplefilter("ignore") + import tensorflow as tf + +tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +tf.get_logger().setLevel('INFO') +tf.autograph.set_verbosity(0) +import warnings + +warnings.filterwarnings("ignore", message=r"Passing", category=FutureWarning) +warnings.simplefilter(action='ignore', category=FutureWarning) +warnings.simplefilter(action='ignore', category=Warning) + +from baselines.ppo2 import sppo2 +from baselines.common.models import build_skill_impala_cnn +from baselines.common.mpi_util import setup_mpi_gpus +from procgen import ProcgenEnv +from baselines.common.vec_env import ( + VecExtractDictObs, + VecMonitor, + VecNormalize +) +from baselines import logger +from mpi4py import MPI +import argparse +import matplotlib.colors as c +import gym +from PIL import Image +import matplotlib +import matplotlib.pyplot as plt +from baselines.common import set_global_seeds +from train_procgen.utils import * +from copy import deepcopy +from matplotlib.offsetbox import OffsetImage, AnnotationBbox + +parser = argparse.ArgumentParser(description='Process procgen enjoying arguments.') +parser.add_argument('--env_name', type=str, default='coinrun') +parser.add_argument('--num_envs', type=int, default=64) +parser.add_argument('--distribution_mode', type=str, default='easy', + choices=["easy", "hard", "exploration", "memory", "extreme"]) +parser.add_argument('--num_levels', type=int, default=0) +parser.add_argument('--start_level', type=int, default=0) +parser.add_argument('--test_worker_interval', type=int, default=0) +parser.add_argument('--timesteps_per_proc', type=int, default=25_000_000) +parser.add_argument('--rand_seed', type=int, default=2021) +parser.add_argument('--num_embeddings', type=int, default=8) +parser.add_argument('--beta', type=float, default=0.0) +parser.add_argument('--alpha1', type=float, default=20) +parser.add_argument('--alpha2', type=float, default=20) +parser.add_argument('--total_states', type=int, default=5000, help='choose how many states you want to have') + +args = parser.parse_args() + + +def isEmpty(path): + if os.path.exists(path) and not os.path.isfile(path): + + # Checking if the directory is empty or not + if not os.listdir(path): + return True + else: + return False + else: + return True + + +def load_model(env_name, num_envs, distribution_mode, num_levels, start_level, + num_embeddings, beta, alpha1, alpha2, + log_dir='./checkpoints', comm=None, rand_seed=None): + ent_coef = .01 + nsteps = 256 + nminibatches = 8 + + mpi_rank_weight = 1 + num_levels = num_levels + + logger.info("creating environment") + venv = ProcgenEnv(num_envs=num_envs, env_name=env_name, num_levels=num_levels, start_level=start_level + 200000, + distribution_mode=distribution_mode, rand_seed=rand_seed) + venv = VecExtractDictObs(venv, "rgb") + + venv = VecMonitor( + venv=venv, filename=None, keep_buf=100, + ) + + venv = VecNormalize(venv=venv, ob=False) + + logger.info("creating tf session") + setup_mpi_gpus() + config = tf.compat.v1.ConfigProto() + config.gpu_options.allow_growth = True # pylint: disable=E1101 + sess = tf.compat.v1.Session(config=config) + sess.__enter__() + + conv_fn = lambda x: build_skill_impala_cnn(x, depths=[16, 32, 32], emb_dim=256, num_embeddings=num_embeddings, + beta=beta, seed=rand_seed) + + logger.info("loading the model") + + model = sppo2.load( + env=venv, + network=conv_fn, + nsteps=nsteps, + nminibatches=nminibatches, + ent_coef=ent_coef, + mpi_rank_weight=mpi_rank_weight, + comm=comm, + vf_coef=0.5, + max_grad_norm=0.5, + seed=rand_seed, + load_path=log_dir, + alpha1=alpha1, + alpha2=alpha2, + ) + + return model + + +def main(): + cluster_path = './train_procgen/figures/' + args.env_name + '_cluster_images' + # if empty, we need to collect states first + if isEmpty(cluster_path): + plt.rcParams.update({'font.size': 15}) + comm = MPI.COMM_WORLD + + saved_dir = './train_procgen/checkpoints/sppo-' + args.env_name + '_' + args.distribution_mode + '_' + str( + args.num_levels) + '_' + str(args.start_level) + '_' + str( + args.rand_seed) + + saved_dir += '/checkpoints/' + str(args.timesteps_per_proc) + + model = load_model(args.env_name, + 1, + args.distribution_mode, + args.num_levels, + args.start_level, + args.num_embeddings, + comm=comm, + log_dir=saved_dir, + rand_seed=args.rand_seed, + beta=args.beta, + alpha1=args.alpha1, + alpha2=args.alpha2, + ) + + if not os.path.isdir(cluster_path): + if not os.path.isdir('./train_procgen/figures'): + os.mkdir('./train_procgen/figures') + os.mkdir(cluster_path) + for i in range(args.num_embeddings): + if not os.path.isdir(cluster_path + '/cluster_' + str(i)): + os.mkdir(cluster_path + '/cluster_' + str(i)) + + # generate the skill clusters + set_global_seeds(args.rand_seed) + env_name = "procgen:procgen-" + args.env_name + "-v0" + env = gym.make(env_name, num_levels=args.num_levels, start_level=args.start_level, + distribution_mode=args.distribution_mode, rand_seed=args.rand_seed, render_mode="human") + obs = env.reset() + counters = np.zeros(args.num_embeddings) + episode_i = 0 + total_states = args.total_states + upper_frames = args.total_states * 0.2 + episode_files = [] + sequence_files = [[] for _ in range(args.num_embeddings)] + sequence_indices = [[] for _ in range(args.num_embeddings)] + episode_rewards = 0 + episode_counters = np.zeros(args.num_embeddings) + # avoid to collect the endless loop + if args.env_name not in ['starpilot', 'fruitbot']: + episode_max_steps = 200 + else: + episode_max_steps = math.inf + + while True: + rgb_img = env.render(mode="rgb_array") + im = Image.fromarray(rgb_img) + a, v, pure_latent, vq_latent, pure_vq_latent, vq_embeddings, encoding_indices, sl, lat = model.skill_step( + obs) + action = np.squeeze(a) + if episode_counters[encoding_indices[0][0]] < upper_frames: + saved_dir = cluster_path + '/cluster_' + str(encoding_indices[0][0]) + file_name = str(episode_i + 1) + '_' + str(sl[0][0]) + '_' + str(sl[0][1]) + '.png' + im.save(saved_dir + '/' + file_name, dpi=(5, 5)) + episode_files.append(saved_dir + '/' + file_name) + if len(sequence_files[encoding_indices[0][0]]) == 0: + sequence_files[encoding_indices[0][0]].append(saved_dir + '/' + file_name) + sequence_indices[encoding_indices[0][0]].append(episode_i + 1) + else: + if (episode_i + 1 - int(sequence_indices[encoding_indices[0][0]][-1])) == 1: + sequence_files[encoding_indices[0][0]].append(saved_dir + '/' + file_name) + sequence_indices[encoding_indices[0][0]].append(episode_i + 1) + else: + if len(sequence_files[encoding_indices[0][0]]) > 20: + for f in sequence_files[encoding_indices[0][0]]: + if os.path.isfile(f): + os.remove(f) + episode_counters[encoding_indices[0][0]] -= 1 + else: + continue + + sequence_files[encoding_indices[0][0]] = [] + sequence_indices[encoding_indices[0][0]] = [] + sequence_files[encoding_indices[0][0]].append(saved_dir + '/' + file_name) + sequence_indices[encoding_indices[0][0]].append(episode_i + 1) + + obs, reward, done, info = env.step(action) + episode_rewards += reward + episode_counters[encoding_indices[0][0]] += 1 + episode_i += 1 + + if done: + if len(episode_files) > episode_max_steps: + for f in episode_files: + if os.path.isfile(f): + os.remove(f) + for i in range(args.num_embeddings): + if f in sequence_files[i]: + sequence_indices[i].pop(sequence_files[i].index(f)) + sequence_files[i].remove(f) + else: + continue + + episode_counters = deepcopy(counters) + counters = deepcopy(episode_counters) + + episode_files = [] + episode_rewards = 0 + + if np.sum(counters) >= total_states: + env.close() + break + + skill_latents = [] + cluster_indices = [] + images = [] + + for i in range(args.num_embeddings): + files = os.listdir(cluster_path + '/cluster_' + str(i)) + for file in files: + if file.endswith('png'): + _, sl_x, sl_y = file[:-4].split('_') + arr_img = plt.imread(cluster_path + '/cluster_' + str(i) + '/' + file) + images.append(arr_img) + skill_latents.append([float(sl_x), float(sl_y)]) + cluster_indices.append(i) + + images = np.array(images) + skill_latents = np.array(skill_latents) + cluster_indices = np.array(cluster_indices) + + fig, ax = plt.subplots() + fig.set_figheight(6) + fig.set_figwidth(8) + np.random.seed(0) + color_set = [(np.random.rand(), np.random.rand(), np.random.rand()) for _ in range(args.num_embeddings)] + + c_cet = [] + for cs in color_set: + c_cet.append(c.to_hex(cs)) + c_cet = np.array(c_cet) + + figure = plt.scatter(skill_latents[:, 0], skill_latents[:, 1], s=10, c=c_cet[cluster_indices], alpha=0.5) + line, = plt.plot(skill_latents[:, 0], skill_latents[:, 1], ls="") + + plt.title('The FDR space') + plt.xlabel('FDR x') + plt.ylabel('FDR y') + + # create the annotations box + im = OffsetImage(images[0, :, :, :], zoom=0.2) + xybox = (100., 100.) + ab = AnnotationBbox(im, (0, 0), xybox=xybox, xycoords='data', + boxcoords="offset points", pad=0.3, arrowprops=dict(arrowstyle="->")) + # add it to the axes and make it invisible + ax.add_artist(ab) + ab.set_visible(False) + + def hover(event): + # if the mouse is over the scatter points + if line.contains(event)[0]: + # find out the index within the array from the event + try: + ind, = line.contains(event)[1]["ind"] + except: + indices = line.contains(event)[1]["ind"] + ind = indices[0] + # get the figure size + w, h = fig.get_size_inches() * fig.dpi + ws = (event.x > w / 2.) * -1 + (event.x <= w / 2.) + hs = (event.y > h / 2.) * -1 + (event.y <= h / 2.) + # if event occurs in the top or right quadrant of the figure, + # change the annotation box position relative to mouse. + ab.xybox = (xybox[0] * ws, xybox[1] * hs) + # make annotation box visible + ab.set_visible(True) + # place it at the position of the hovered scatter point + ab.xy = (skill_latents[:, 0][ind], skill_latents[:, 1][ind]) + # set the image corresponding to that point + im.set_data(images[ind, :, :, :]) + else: + # if the mouse is not over a scatter point + ab.set_visible(False) + fig.canvas.draw_idle() + + # add callback for mouse moves + fig.canvas.mpl_connect('motion_notify_event', hover) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/train_procgen/single_graph.py b/train_procgen/single_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..0611d8310dadba85f10c0cd7a8fcc540918a4e41 --- /dev/null +++ b/train_procgen/single_graph.py @@ -0,0 +1,99 @@ +from single_graph_util import plot_experiment, switch_to_outer_plot +from constants import ENV_NAMES, NAME_TO_CASE, EASY_GAME_RANGES, HARD_GAME_RANGES + +import matplotlib +import matplotlib.pyplot as plt + +import argparse +from utils import str2bool +import os + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--env_name', type=str, default='coinrun') + parser.add_argument('--distribution_mode', type=str, default='easy', + help="Environment distribution_mode ('easy' or 'hard')") + parser.add_argument('--normalize_and_reduce', dest='normalize_and_reduce', action='store_true') + parser.add_argument('--restrict_training_set', type=str2bool, default=False, + help='True for 200 levels; False for all distribution') + parser.add_argument('--save', dest='save', action='store_false') + args = parser.parse_args() + + run_directory_prefix = main_pcg_sample_entry(args.env_name, args.distribution_mode, args.normalize_and_reduce, + args.restrict_training_set) + plt.rcParams.update({'font.size': 20}) + plt.tight_layout() + + if args.save: + if not os.path.isdir('figures'): + os.makedirs('figures') + + suffix = '-mean' if args.normalize_and_reduce else '' + plt.savefig(f'figures/{run_directory_prefix}{suffix}.png') + plt.show() + else: + plt.show() + + +def main_pcg_sample_entry(env_name, distribution_mode, normalize_and_reduce, restrict_training_set): + params = { + 'xtick.labelsize': 12, + 'ytick.labelsize': 12, + 'axes.titlesize': 16, + 'axes.labelsize': 24, + 'legend.fontsize': 18, + 'figure.figsize': [9, 9] + } + matplotlib.rcParams.update(params) + + kwargs = {'smoothing': .9} + + if distribution_mode == 'easy': + kwargs[ + 'x_scale'] = 1 * 256 * 64 / 1e6 # num_workers * num_steps_per_rollout * num_envs_per_worker / graph_scaling + num_train_levels = 200 + normalization_ranges = EASY_GAME_RANGES + elif distribution_mode == 'hard': + kwargs[ + 'x_scale'] = 4 * 256 * 64 / 1e6 # num_workers * num_steps_per_rollout * num_envs_per_worker / graph_scaling + num_train_levels = 500 + normalization_ranges = HARD_GAME_RANGES + else: + assert False, "specify distribution_mode as 'easy' or 'hard'" + + y_label = 'Score' + x_label = 'Timesteps (Millions)' + + run_directory_prefix = f"{env_name}_{distribution_mode}_{num_train_levels if restrict_training_set else '0'}" + kwargs['run_directory_prefix'] = f"{run_directory_prefix}" + + # We throw out the first few datapoints to give the episodic reward buffers time to fill up + # Otherwise, there could be a short-episode bias + kwargs['env_name'] = env_name + kwargs['first_valid'] = 10 + + if restrict_training_set: + kwargs['suffixes'] = ['', 'rank001'] + else: + kwargs['suffixes'] = [''] + + if normalize_and_reduce: + kwargs['normalization_ranges'] = normalization_ranges + y_label = 'Mean Normalized Score' + + fig, axarr = plot_experiment(**kwargs) + + if normalize_and_reduce: + axarr.set_xlabel(x_label, labelpad=10) + axarr.set_ylabel(y_label, labelpad=10) + else: + ax0 = switch_to_outer_plot(fig) + ax0.set_xlabel(x_label, labelpad=40) + ax0.set_ylabel(y_label, labelpad=35) + + return run_directory_prefix + + +if __name__ == '__main__': + main() diff --git a/train_procgen/single_graph_util.py b/train_procgen/single_graph_util.py new file mode 100644 index 0000000000000000000000000000000000000000..d1f0aacf619410d46262010f782a0583c5e5c73f --- /dev/null +++ b/train_procgen/single_graph_util.py @@ -0,0 +1,158 @@ +import csv +import numpy as np +import matplotlib.pyplot as plt +import matplotlib +from math import ceil +from constants import ENV_NAMES + +import seaborn # sets some style parameters automatically + +np.random.seed(1024) +COLORS = [(np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255)) for x in range(20)] + + +def switch_to_outer_plot(fig): + ax0 = fig.add_subplot(111, frame_on=False) + ax0.set_xticks([]) + ax0.set_yticks([]) + + return ax0 + + +def ema(data_in, smoothing=0): + data_out = np.zeros_like(data_in) + curr = np.nan + + for i in range(len(data_in)): + x = data_in[i] + if np.isnan(curr): + curr = x + else: + curr = (1 - smoothing) * x + smoothing * curr + + data_out[i] = curr + + return data_out + + +def plot_data_mean_std(ax, data_y, color_idx=0, data_x=None, x_scale=1, smoothing=0, first_valid=0, label=None): + color = COLORS[color_idx] + hexcolor = '#%02x%02x%02x' % color + + data_y = data_y[:, first_valid:] + nx, num_datapoint = np.shape(data_y) + + if smoothing > 0: + for i in range(nx): + data_y[i, ...] = ema(data_y[i, ...], smoothing) + + if data_x is None: + data_x = (np.array(range(num_datapoint)) + first_valid) * x_scale + + data_mean = np.mean(data_y, axis=0) + data_std = np.std(data_y, axis=0, ddof=1) + + ax.plot(data_x, data_mean, color=hexcolor, label=label, linestyle='solid', alpha=1, rasterized=True) + ax.fill_between(data_x, data_mean - data_std, data_mean + data_std, color=hexcolor, alpha=.25, linewidth=0.0, + rasterized=True) + + +def read_csv(filename, key_name): + with open(filename) as csv_file: + csv_reader = csv.reader(csv_file, delimiter=',') + key_index = -1 + + values = [] + + for line_num, row in enumerate(csv_reader): + row = [x.lower() for x in row] + if line_num == 0: + idxs = [i for i, val in enumerate(row) if val == key_name] + key_index = idxs[0] + else: + values.append(row[key_index]) + + return np.array(values, dtype=np.float32) + + +def plot_values(ax, all_values, title=None, max_x=0, label=None, **kwargs): + if max_x > 0: + all_values = all_values[..., :max_x] + + if ax is not None: + plot_data_mean_std(ax, all_values, label=label, **kwargs) + ax.set_title(title, fontsize=20) + + return all_values + + +def plot_experiment(env_name, run_directory_prefix, titles=None, suffixes=[''], normalization_ranges=None, + key_name='eprewmean', **kwargs): + run_folders = [f'{run_directory_prefix}_{0}_{2020 + x}' for x in range(3)] + sppo_run_folders = ['sppo-' + rf for rf in run_folders] + ppo_run_folders = ['ppo-' + rf for rf in run_folders] + run_folders = [sppo_run_folders, ppo_run_folders] + run_names = ['SPPO', 'PPO'] + + num_envs = 1 + will_normalize_and_reduce = normalization_ranges is not None + + if will_normalize_and_reduce: + num_visible_plots = 1 + f, axarr = plt.subplots() + else: + num_visible_plots = num_envs + dimx = dimy = ceil(np.sqrt(num_visible_plots)) + f, axarr = plt.subplots(dimx, dimy, sharex=True) + + color_idx = 0 + for rf in range(len(run_folders)): + for suffix in suffixes: + all_values = [] + game_weights = [1] * num_envs + + if len(suffixes) == 1: + label = run_names[rf] + else: + if suffix == '': + label = run_names[rf] + ' train' + else: + label = run_names[rf] + ' test' + + print(f'loading results from {env_name}...') + + if num_visible_plots == 1: + ax = axarr + else: + dimy = len(axarr[0]) + ax = axarr[0 // dimy][0 % dimy] + + csv_files = [f"checkpoints/{resid}/progress{'-' if len(suffix) > 0 else ''}{suffix}.csv" for resid in + run_folders[rf]] + curr_ax = None if will_normalize_and_reduce else ax + + raw_data = np.array([read_csv(file, key_name) for file in csv_files]) + values = plot_values(curr_ax, raw_data, title=env_name, color_idx=color_idx, label=label, **kwargs) + + if will_normalize_and_reduce: + game_range = normalization_ranges[env_name] + game_min = game_range[0] + game_max = game_range[1] + game_delta = game_max - game_min + sub_values = game_weights[0] * (np.array(values) - game_min) / (game_delta) + all_values.append(sub_values) + + if will_normalize_and_reduce: + normalized_data = np.sum(all_values, axis=0) + normalized_data = normalized_data / np.sum(game_weights) + title = 'Mean Normalized Score' + plot_values(ax, normalized_data, title=None, color_idx=color_idx, label=suffix, **kwargs) + + color_idx += 1 + + if num_visible_plots == 1: + ax.legend(loc='lower right') + else: + f.legend(loc='lower right', bbox_to_anchor=(.5, 0, .5, 1)) + + return f, axarr diff --git a/train_procgen/train_ppo.py b/train_procgen/train_ppo.py new file mode 100644 index 0000000000000000000000000000000000000000..b95216d33269874b16f9ae277aa15a244654c948 --- /dev/null +++ b/train_procgen/train_ppo.py @@ -0,0 +1,143 @@ +import sys + +if not sys.warnoptions: + import warnings + + warnings.simplefilter("ignore") + +with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=FutureWarning) + warnings.filterwarnings("ignore", category=RuntimeWarning) + warnings.simplefilter("ignore") + import tensorflow as tf + +tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +tf.get_logger().setLevel('INFO') +tf.autograph.set_verbosity(0) +import warnings + +warnings.filterwarnings("ignore", message=r"Passing", category=FutureWarning) +warnings.simplefilter(action='ignore', category=FutureWarning) +warnings.simplefilter(action='ignore', category=Warning) + +from baselines.ppo2 import ppo2 +from baselines.common.models import build_impala_cnn +from baselines.common.mpi_util import setup_mpi_gpus +from procgen import ProcgenEnv +from baselines.common.vec_env import ( + VecExtractDictObs, + VecMonitor, + VecNormalize +) +from baselines import logger +from mpi4py import MPI +import argparse + +parser = argparse.ArgumentParser(description='Process procgen training arguments.') +parser.add_argument('--env_name', type=str, default='coinrun') +parser.add_argument('--num_envs', type=int, default=64) +parser.add_argument('--distribution_mode', type=str, default='hard', + choices=["easy", "hard", "exploration", "memory", "extreme"]) +parser.add_argument('--num_levels', type=int, default=0) +parser.add_argument('--start_level', type=int, default=0) +parser.add_argument('--test_worker_interval', type=int, default=0) +parser.add_argument('--timesteps_per_proc', type=int, default=25_000_000) +parser.add_argument('--rand_seed', type=int, default=2022) + +args = parser.parse_args() + + +def train_fn(env_name, num_envs, distribution_mode, num_levels, start_level, timesteps_per_proc, is_test_worker=False, + log_dir='./checkpoints', comm=None, rand_seed=None): + learning_rate = 5e-4 + ent_coef = .01 + gamma = .999 + lam = .95 + nsteps = 256 + nminibatches = 8 + ppo_epochs = 3 + clip_range = .2 + use_vf_clipping = True + + mpi_rank_weight = 0 if is_test_worker else 1 + num_levels = 0 if is_test_worker else num_levels + + if log_dir is not None: + log_comm = comm.Split(1 if is_test_worker else 0, 0) + format_strs = ['csv', 'stdout'] if log_comm.Get_rank() == 0 else [] + logger.configure(comm=log_comm, dir=log_dir, format_strs=format_strs) + + logger.info("creating environment") + venv = ProcgenEnv(num_envs=num_envs, env_name=env_name, num_levels=num_levels, start_level=start_level, + distribution_mode=distribution_mode, rand_seed=rand_seed) + venv = VecExtractDictObs(venv, "rgb") + + venv = VecMonitor( + venv=venv, filename=None, keep_buf=100, + ) + + venv = VecNormalize(venv=venv, ob=False) + + logger.info("creating tf session") + setup_mpi_gpus() + config = tf.compat.v1.ConfigProto() + config.gpu_options.allow_growth = True # pylint: disable=E1101 + sess = tf.compat.v1.Session(config=config) + sess.__enter__() + + conv_fn = lambda x: build_impala_cnn(x, depths=[16, 32, 32], emb_size=256) + + logger.info("training") + ppo2.learn( + env=venv, + network=conv_fn, + total_timesteps=timesteps_per_proc, + save_interval=0, + nsteps=nsteps, + nminibatches=nminibatches, + lam=lam, + gamma=gamma, + noptepochs=ppo_epochs, + log_interval=1, + ent_coef=ent_coef, + mpi_rank_weight=mpi_rank_weight, + clip_vf=use_vf_clipping, + comm=comm, + lr=learning_rate, + cliprange=clip_range, + update_fn=None, + init_fn=None, + vf_coef=0.5, + max_grad_norm=0.5, + seed=rand_seed + ) + + +def main(): + comm = MPI.COMM_WORLD + rank = comm.Get_rank() + + is_test_worker = False + test_worker_interval = args.test_worker_interval + + if test_worker_interval > 0: + is_test_worker = rank % test_worker_interval == (test_worker_interval - 1) + + saved_dir = './train_procgen/checkpoints/ppo-' + args.env_name + '_' + args.distribution_mode + '_' + str( + args.num_levels) + '_' + str(args.start_level) + '_' + str( + args.rand_seed) + + train_fn(args.env_name, + args.num_envs, + args.distribution_mode, + args.num_levels, + args.start_level, + args.timesteps_per_proc, + is_test_worker=is_test_worker, + comm=comm, + log_dir=saved_dir, + rand_seed=args.rand_seed) + + +if __name__ == '__main__': + main() diff --git a/train_procgen/train_sppo.py b/train_procgen/train_sppo.py new file mode 100644 index 0000000000000000000000000000000000000000..a790abb563655cbec4d103b78f7347ca935ead7d --- /dev/null +++ b/train_procgen/train_sppo.py @@ -0,0 +1,163 @@ +import sys + +if not sys.warnoptions: + import warnings + + warnings.simplefilter("ignore") + +with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=FutureWarning) + warnings.filterwarnings("ignore", category=RuntimeWarning) + warnings.simplefilter("ignore") + import tensorflow as tf + +tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +tf.get_logger().setLevel('INFO') +tf.autograph.set_verbosity(0) +import warnings + +warnings.filterwarnings("ignore", message=r"Passing", category=FutureWarning) +warnings.simplefilter(action='ignore', category=FutureWarning) +warnings.simplefilter(action='ignore', category=Warning) + +# import tensorflow as tf +from baselines.ppo2 import sppo2 +from baselines.common.models import build_skill_impala_cnn +from baselines.common.mpi_util import setup_mpi_gpus +from procgen import ProcgenEnv +from baselines.common.vec_env import ( + VecExtractDictObs, + VecMonitor, + VecNormalize +) +from baselines import logger +from mpi4py import MPI +import argparse +from train_procgen.utils import * +from train_procgen.constants import EASY_GAME_RANGES + +parser = argparse.ArgumentParser(description='Process procgen training arguments.') +parser.add_argument('--env_name', type=str, default='coinrun') +parser.add_argument('--num_envs', type=int, default=64) +parser.add_argument('--distribution_mode', type=str, default='easy', + choices=["easy", "hard", "exploration", "memory", "extreme"]) +parser.add_argument('--num_levels', type=int, default=0) +parser.add_argument('--start_level', type=int, default=0) +parser.add_argument('--test_worker_interval', type=int, default=0) +parser.add_argument('--timesteps_per_proc', type=int, default=25_000_000) +parser.add_argument('--rand_seed', type=int, default=2022) +parser.add_argument('--num_embeddings', type=int, default=8) +parser.add_argument('--beta', type=float, default=0.0) +parser.add_argument('--alpha1', type=float, default=20) +parser.add_argument('--alpha2', type=float, default=20) + +args = parser.parse_args() + + +def train_fn(env_name, num_envs, distribution_mode, num_levels, start_level, timesteps_per_proc, + num_embeddings, beta, alpha1, alpha2, + is_test_worker=False, + log_dir='./train_procgen/checkpoints', comm=None, rand_seed=None): + learning_rate = 5e-4 + ent_coef = .01 + gamma = .999 + lam = .95 + nsteps = 256 + nminibatches = 8 + ppo_epochs = 3 + clip_range = .2 + use_vf_clipping = True + + mpi_rank_weight = 0 if is_test_worker else 1 + num_levels = 0 if is_test_worker else num_levels + + if log_dir is not None: + log_comm = comm.Split(1 if is_test_worker else 0, 0) + format_strs = ['csv', 'stdout'] if log_comm.Get_rank() == 0 else [] + logger.configure(comm=log_comm, dir=log_dir, format_strs=format_strs) + + logger.info("creating environment") + venv = ProcgenEnv(num_envs=num_envs, env_name=env_name, num_levels=num_levels, start_level=start_level, + distribution_mode=distribution_mode, rand_seed=rand_seed) + venv = VecExtractDictObs(venv, "rgb") + + venv = VecMonitor( + venv=venv, filename=None, keep_buf=100, + ) + + venv = VecNormalize(venv=venv, ob=False) + + logger.info("creating tf session") + setup_mpi_gpus() + config = tf.compat.v1.ConfigProto() + config.gpu_options.allow_growth = True # pylint: disable=E1101 + + sess = tf.compat.v1.Session(config=config) + sess.__enter__() + + conv_fn = lambda x: build_skill_impala_cnn(x, depths=[16, 32, 32], emb_dim=256, num_embeddings=num_embeddings, + beta=beta, seed=rand_seed) + + logger.info("training") + sppo2.learn( + env=venv, + network=conv_fn, + total_timesteps=timesteps_per_proc, + save_interval=0, + nsteps=nsteps, + nminibatches=nminibatches, + lam=lam, + gamma=gamma, + noptepochs=ppo_epochs, + log_interval=1, + ent_coef=ent_coef, + mpi_rank_weight=mpi_rank_weight, + clip_vf=use_vf_clipping, + comm=comm, + lr=learning_rate, + cliprange=clip_range, + update_fn=None, + init_fn=None, + vf_coef=0.5, + max_grad_norm=0.5, + seed=rand_seed, + num_embeddings=num_embeddings, + highest_score=EASY_GAME_RANGES[env_name][1], + alpha1=alpha1, + alpha2=alpha2, + ) + + +def main(): + comm = MPI.COMM_WORLD + rank = comm.Get_rank() + + is_test_worker = False + test_worker_interval = args.test_worker_interval + + if test_worker_interval > 0: + is_test_worker = rank % test_worker_interval == (test_worker_interval - 1) + + saved_dir = './train_procgen/checkpoints/sppo-' + args.env_name + '_' + args.distribution_mode + '_' + str( + args.num_levels) + '_' + str(args.start_level) + '_' + str( + args.rand_seed) + + train_fn(args.env_name, + args.num_envs, + args.distribution_mode, + args.num_levels, + args.start_level, + args.timesteps_per_proc, + is_test_worker=is_test_worker, + comm=comm, + log_dir=saved_dir, + rand_seed=args.rand_seed, + num_embeddings=args.num_embeddings, + beta=args.beta, + alpha1=args.alpha1, + alpha2=args.alpha2, + ) + + +if __name__ == '__main__': + main() diff --git a/train_procgen/utils.py b/train_procgen/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..36065cbc08317fe7932fada9cf8b14212afb1144 --- /dev/null +++ b/train_procgen/utils.py @@ -0,0 +1,47 @@ +import argparse +from collections import OrderedDict +import numpy as np +import os +import cv2 +from os.path import isfile, join + + +def str2bool(v): + if isinstance(v, bool): + return v + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected.') + + +def convert_frames_to_video(pathIn, pathOut, fps=8, limits=480): + frame_array = [] + files = [f for f in os.listdir(pathIn) if isfile(join(pathIn, f)) and ( + join(pathIn, f).endswith(".jpg") or join(pathIn, f).endswith(".jpeg") or join(pathIn, f).endswith( + "png"))] + if len(files) > limits: + files = files[:limits] + if len(files) == 0: + return + # for sorting the file names properly + files.sort(key=lambda x: int(x.split("_")[0])) + for i in range(len(files)): + filename = pathIn + files[i] + if filename.endswith(".jpg") or filename.endswith(".jpeg") or filename.endswith("png"): + # reading each files + img = cv2.imread(filename) + # resize the image for saving space + img_resized = cv2.resize(img, (256, 256)) + height, width, layers = img_resized.shape + size = (width, height) + # inserting the frames into an image array + frame_array.append(img_resized) + + out = cv2.VideoWriter(pathOut, cv2.VideoWriter_fourcc(*'DIVX'), fps, size) + for i in range(len(frame_array)): + # writing to a image array + out.write(frame_array[i]) + out.release() diff --git a/videos/fruitbot_skills/cluster_0.gif b/videos/fruitbot_skills/cluster_0.gif new file mode 100644 index 0000000000000000000000000000000000000000..6f965c1bcf29004670dcffa1edf5f065c58fb1f3 --- /dev/null +++ b/videos/fruitbot_skills/cluster_0.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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