| | --- |
| | library_name: sample-factory |
| | tags: |
| | - deep-reinforcement-learning |
| | - reinforcement-learning |
| | - sample-factory |
| | model-index: |
| | - name: APPO |
| | results: |
| | - task: |
| | type: reinforcement-learning |
| | name: reinforcement-learning |
| | dataset: |
| | name: mujoco_pendulum |
| | type: mujoco_pendulum |
| | metrics: |
| | - type: mean_reward |
| | value: 1000.00 +/- 0.00 |
| | name: mean_reward |
| | verified: false |
| | --- |
| | |
| | ## About the Project |
| |
|
| | 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). |
| |
|
| | 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. |
| |
|
| | ## Project Aims |
| |
|
| | 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. |
| |
|
| | 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. |
| |
|
| | 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. |
| |
|
| | 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') |
| |
|
| |
|
| | ## About the Model |
| |
|
| | 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: |
| | ``` |
| | hyperparameters = { |
| | "help": false, |
| | "algo": "APPO", |
| | "env": "atari_asteroid", |
| | "experiment": "atari_asteroid_APPO", |
| | "train_dir": "./train_atari", |
| | "restart_behavior": "restart", |
| | "device": "gpu", |
| | "seed": 1234, |
| | "num_policies": 2, |
| | "async_rl": true, |
| | "serial_mode": false, |
| | "batched_sampling": true, |
| | "num_batches_to_accumulate": 2, |
| | "worker_num_splits": 1, |
| | "policy_workers_per_policy": 1, |
| | "max_policy_lag": 1000, |
| | "num_workers": 16, |
| | "num_envs_per_worker": 2, |
| | "batch_size": 1024, |
| | "num_batches_per_epoch": 8, |
| | "num_epochs": 4, |
| | "rollout": 128, |
| | "recurrence": 1, |
| | "shuffle_minibatches": false, |
| | "gamma": 0.99, |
| | "reward_scale": 1.0, |
| | "reward_clip": 1000.0, |
| | "value_bootstrap": false, |
| | "normalize_returns": true, |
| | "exploration_loss_coeff": 0.0004677351413, |
| | "value_loss_coeff": 0.5, |
| | "kl_loss_coeff": 0.0, |
| | "exploration_loss": "entropy", |
| | "gae_lambda": 0.95, |
| | "ppo_clip_ratio": 0.1, |
| | "ppo_clip_value": 1.0, |
| | "with_vtrace": true, |
| | "vtrace_rho": 1.0, |
| | "vtrace_c": 1.0, |
| | "optimizer": "adam", |
| | "adam_eps": 1e-05, |
| | "adam_beta1": 0.9, |
| | "adam_beta2": 0.999, |
| | "max_grad_norm": 0.0, |
| | "learning_rate": 0.0003033891184, |
| | "lr_schedule": "linear_decay", |
| | "lr_schedule_kl_threshold": 0.008, |
| | "lr_adaptive_min": 1e-06, |
| | "lr_adaptive_max": 0.01, |
| | "obs_subtract_mean": 0.0, |
| | "obs_scale": 255.0, |
| | "normalize_input": true, |
| | "normalize_input_keys": [ |
| | "obs" |
| | ], |
| | "decorrelate_experience_max_seconds": 0, |
| | "decorrelate_envs_on_one_worker": true, |
| | "actor_worker_gpus": [], |
| | "set_workers_cpu_affinity": true, |
| | "force_envs_single_thread": false, |
| | "default_niceness": 0, |
| | "log_to_file": true, |
| | "experiment_summaries_interval": 3, |
| | "flush_summaries_interval": 30, |
| | "stats_avg": 100, |
| | "summaries_use_frameskip": true, |
| | "heartbeat_interval": 10, |
| | "heartbeat_reporting_interval": 60, |
| | "train_for_env_steps": 100000000, |
| | "train_for_seconds": 10000000000, |
| | "save_every_sec": 120, |
| | "keep_checkpoints": 2, |
| | "load_checkpoint_kind": "latest", |
| | "save_milestones_sec": 1200, |
| | "save_best_every_sec": 5, |
| | "save_best_metric": "reward", |
| | "save_best_after": 100000, |
| | "benchmark": false, |
| | "encoder_mlp_layers": [ |
| | 512, |
| | 512 |
| | ], |
| | "encoder_conv_architecture": "convnet_atari", |
| | "encoder_conv_mlp_layers": [ |
| | 512 |
| | ], |
| | "use_rnn": false, |
| | "rnn_size": 512, |
| | "rnn_type": "gru", |
| | "rnn_num_layers": 1, |
| | "decoder_mlp_layers": [], |
| | "nonlinearity": "relu", |
| | "policy_initialization": "orthogonal", |
| | "policy_init_gain": 1.0, |
| | "actor_critic_share_weights": true, |
| | "adaptive_stddev": false, |
| | "continuous_tanh_scale": 0.0, |
| | "initial_stddev": 1.0, |
| | "use_env_info_cache": false, |
| | "env_gpu_actions": false, |
| | "env_gpu_observations": true, |
| | "env_frameskip": 4, |
| | "env_framestack": 4, |
| | "pixel_format": "CHW" |
| | } |
| | |
| | ``` |
| |
|
| |
|
| | |
| | A(n) **APPO** impala model trained on the **mujoco_pendulum** environment. |
| | |
| | This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Sample factory is a |
| | high throughput on-policy RL framework. I have been using |
| | Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ |
| |
|
| |
|
| | ## Downloading the model |
| |
|
| | After installing Sample-Factory, download the model with: |
| | ``` |
| | python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-mujoco_pendulum |
| | ``` |
| |
|
| | |
| | ## Using the model |
| | |
| | To run the model after download, use the `enjoy` script corresponding to this environment: |
| | ``` |
| | python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_pendulum --train_dir=./train_dir --experiment=APPO-mujoco_pendulum |
| | ``` |
| |
|
| |
|
| | You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. |
| | See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details |
| | |
| | ## Training with this model |
| | |
| | To continue training with this model, use the `train` script corresponding to this environment: |
| | ``` |
| | python -m sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_pendulum --train_dir=./train_dir --experiment=APPO-mujoco_pendulum --restart_behavior=resume --train_for_env_steps=10000000000 |
| | ``` |
| |
|
| | 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. |
| | |
