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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_reacher |
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type: mujoco_reacher |
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metrics: |
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- type: mean_reward |
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value: -4.70 +/- 1.34 |
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name: mean_reward |
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verified: false |
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--- |
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## About the Project |
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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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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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## Project Aims |
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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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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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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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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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## About the Model |
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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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A(n) **APPO** impala model trained on the **mujoco_reacher** environment. |
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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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## Downloading the model |
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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_reacher |
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``` |
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## Using the model |
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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_reacher --train_dir=./train_dir --experiment=APPO-mujoco_reacher |
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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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## Training with this model |
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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_reacher --train_dir=./train_dir --experiment=APPO-mujoco_reacher --restart_behavior=resume --train_for_env_steps=10000000000 |
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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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