| --- |
| 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: atari_zaxxon |
| type: atari_zaxxon |
| metrics: |
| - type: mean_reward |
| value: 45280.00 +/- 14865.72 |
| 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": false, |
| "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** model trained on the **atari_zaxxon** 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-atari_zaxxon |
| ``` |
|
|
| |
| ## Using the model |
| |
| To run the model after download, use the `enjoy` script corresponding to this environment: |
| ``` |
| python -m sf_examples.atari.enjoy_atari --algo=APPO --env=atari_zaxxon --train_dir=./train_dir --experiment=APPO-atari_zaxxon |
| ``` |
|
|
|
|
| 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.atari.train_atari --algo=APPO --env=atari_zaxxon --train_dir=./train_dir --experiment=APPO-atari_zaxxon --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. |
| |
