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appo-mujoco_doublependulum-sota

Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
Eval Results (legacy)
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MattStammers commited on
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+ ---
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+ library_name: sample-factory
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+ tags:
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - sample-factory
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+ model-index:
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+ - name: APPO
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: mujoco_doublependulum
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+ type: mujoco_doublependulum
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+ metrics:
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+ - type: mean_reward
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+ value: 7575.65 +/- 3557.95
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ ## About the Project
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+
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+ This project is an attempt to maximise performance of high sample throughput APPO RL models in Atari environments in as carbon efficient a manner as possible using a single, not particularly high performance single machine. It is about demonstrating the generalisability of on-policy algorithms to create good performance quickly (by sacrificing sample efficiency) while also proving that this route to RL production is accessible to even hobbyists like me (I am a gastroenterologist not a computer scientist).
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+
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+ In terms of throughput I am managing to reach throughputs of 2,500 - 3,000 across both policies using sample factory using two Quadro P2200's (not particularly powerful GPUs) each loaded up about 60% (3GB). Previously using the stable baselines 3 (sb3) implementation of PPO it would take about a week to train an atari agent to 100 million timesteps synchronously. By comparison the sample factory async implementation takes only just over 2 hours to achieve the same result. That is about 84 times faster with only typically a 21 watt burn per GPU. I am thus very grateful to Alex Petrenko and all the sample factory team for their work on this.
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+
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+ ## Project Aims
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+
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+ This model as with all the others in the benchmarks was trained initially asynchronously un-seeded to 10 million steps for the purposes of setting a sample factory async baseline for this model on this environment but only 3/57 made it anywhere near sota performance.
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+
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+ I then re-trained the models with 100 million timesteps- at this point 2 environments maxed out at sota performance (Pong and Freeway) with four approaching sota performance - (atlantis, boxing, tennis and fishingderby.) =6/57 near sota.
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+
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+ The aim now is to try and reach state-of-the-art (SOTA) performance on a further block of atari environments using up to 1 billion training timesteps initially with appo. I will flag the models with SOTA when they reach at or near these levels.
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+
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+ After this I will switch on V-Trace to see if the Impala variations perform any better with the same seed (I have seeded '1234')
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+
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+
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+ ## About the Model
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+
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+ The hyperparameters used in the model are described in my shell script on my fork of sample-factory: https://github.com/MattStammers/sample-factory. Given that https://huggingface.co/edbeeching has kindly shared his parameters, I saved time and energy by using many of his tuned hyperparameters to reduce carbon inefficiency:
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+ ```
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+ hyperparameters = {
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+ "help": false,
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+ "algo": "APPO",
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+ "env": "atari_asteroid",
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+ "experiment": "atari_asteroid_APPO",
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+ "train_dir": "./train_atari",
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+ "restart_behavior": "restart",
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+ "device": "gpu",
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+ "seed": 1234,
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+ "num_policies": 2,
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+ "async_rl": true,
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+ "serial_mode": false,
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+ "batched_sampling": true,
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+ "num_batches_to_accumulate": 2,
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+ "worker_num_splits": 1,
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+ "policy_workers_per_policy": 1,
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+ "max_policy_lag": 1000,
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+ "num_workers": 16,
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+ "num_envs_per_worker": 2,
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+ "batch_size": 1024,
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+ "num_batches_per_epoch": 8,
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+ "num_epochs": 4,
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+ "rollout": 128,
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+ "recurrence": 1,
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+ "shuffle_minibatches": false,
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+ "gamma": 0.99,
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+ "reward_scale": 1.0,
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+ "reward_clip": 1000.0,
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+ "value_bootstrap": false,
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+ "normalize_returns": true,
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+ "exploration_loss_coeff": 0.0004677351413,
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+ "value_loss_coeff": 0.5,
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+ "kl_loss_coeff": 0.0,
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+ "exploration_loss": "entropy",
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+ "gae_lambda": 0.95,
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+ "ppo_clip_ratio": 0.1,
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+ "ppo_clip_value": 1.0,
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+ "with_vtrace": true,
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+ "vtrace_rho": 1.0,
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+ "vtrace_c": 1.0,
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+ "optimizer": "adam",
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+ "adam_eps": 1e-05,
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+ "adam_beta1": 0.9,
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+ "adam_beta2": 0.999,
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+ "max_grad_norm": 0.0,
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+ "learning_rate": 0.0003033891184,
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+ "lr_schedule": "linear_decay",
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+ "lr_schedule_kl_threshold": 0.008,
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+ "lr_adaptive_min": 1e-06,
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+ "lr_adaptive_max": 0.01,
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+ "obs_subtract_mean": 0.0,
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+ "obs_scale": 255.0,
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+ "normalize_input": true,
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+ "normalize_input_keys": [
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+ "obs"
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+ ],
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+ "decorrelate_experience_max_seconds": 0,
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+ "decorrelate_envs_on_one_worker": true,
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+ "actor_worker_gpus": [],
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+ "set_workers_cpu_affinity": true,
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+ "force_envs_single_thread": false,
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+ "default_niceness": 0,
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+ "log_to_file": true,
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+ "experiment_summaries_interval": 3,
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+ "flush_summaries_interval": 30,
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+ "stats_avg": 100,
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+ "summaries_use_frameskip": true,
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+ "heartbeat_interval": 10,
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+ "heartbeat_reporting_interval": 60,
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+ "train_for_env_steps": 100000000,
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+ "train_for_seconds": 10000000000,
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+ "save_every_sec": 120,
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+ "keep_checkpoints": 2,
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+ "load_checkpoint_kind": "latest",
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+ "save_milestones_sec": 1200,
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+ "save_best_every_sec": 5,
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+ "save_best_metric": "reward",
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+ "save_best_after": 100000,
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+ "benchmark": false,
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+ "encoder_mlp_layers": [
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+ 512,
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+ 512
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+ ],
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+ "encoder_conv_architecture": "convnet_atari",
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+ "encoder_conv_mlp_layers": [
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+ 512
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+ ],
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+ "use_rnn": false,
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+ "rnn_size": 512,
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+ "rnn_type": "gru",
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+ "rnn_num_layers": 1,
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+ "decoder_mlp_layers": [],
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+ "nonlinearity": "relu",
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+ "policy_initialization": "orthogonal",
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+ "policy_init_gain": 1.0,
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+ "actor_critic_share_weights": true,
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+ "adaptive_stddev": false,
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+ "continuous_tanh_scale": 0.0,
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+ "initial_stddev": 1.0,
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+ "use_env_info_cache": false,
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+ "env_gpu_actions": false,
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+ "env_gpu_observations": true,
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+ "env_frameskip": 4,
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+ "env_framestack": 4,
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+ "pixel_format": "CHW"
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+ }
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+
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+ ```
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+
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+
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+
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+ A(n) **APPO** impala model trained on the **mujoco_doublependulum** environment.
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+
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+ This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Sample factory is a
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+ high throughput on-policy RL framework. I have been using
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+ Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
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+
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+
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+ ## Downloading the model
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+
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+ After installing Sample-Factory, download the model with:
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+ ```
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+ python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-mujoco_doublependulum
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+ ```
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+
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+
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+ ## Using the model
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+
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+ To run the model after download, use the `enjoy` script corresponding to this environment:
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+ ```
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+ python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_doublependulum --train_dir=./train_dir --experiment=APPO-mujoco_doublependulum
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+ ```
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+
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+
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+ You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
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+ See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
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+
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+ ## Training with this model
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+
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+ To continue training with this model, use the `train` script corresponding to this environment:
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+ ```
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+ python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_doublependulum --train_dir=./train_dir --experiment=APPO-mujoco_doublependulum --restart_behavior=resume --train_for_env_steps=10000000000
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+ ```
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+
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+ Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
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+
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