(CleanRL) PPO Agent Playing Breakout-v5

This is a trained model of a PPO agent playing Breakout-v5. The model was trained by using CleanRL and the most up-to-date training code can be found here.

Get Started

To use this model, please install the cleanrl package with the following command:

pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_impala_envpool_machado_atari_wrapper --env-id Breakout-v5

Please refer to the documentation for more detail.

Command to reproduce the training

curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_impala_envpool_machado_atari_wrapper-seed1/raw/main/cleanba_impala_envpool_machado_atari_wrapper.py
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_impala_envpool_machado_atari_wrapper-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_impala_envpool_machado_atari_wrapper-seed1/raw/main/poetry.lock
poetry install --all-extras
python cleanba_impala_envpool_machado_atari_wrapper.py --save-model --upload-model --total-timesteps 20000

Hyperparameters

{'actor_device_ids': [0],
 'actor_devices': ['gpu:0'],
 'anneal_lr': True,
 'async_batch_size': 60,
 'async_update': 1,
 'batch_size': 1200,
 'capture_video': False,
 'cuda': True,
 'distributed': False,
 'ent_coef': 0.01,
 'env_id': 'Breakout-v5',
 'exp_name': 'cleanba_impala_envpool_machado_atari_wrapper',
 'gamma': 0.99,
 'global_learner_decices': ['gpu:0'],
 'hf_entity': '',
 'learner_device_ids': [0],
 'learner_devices': ['gpu:0'],
 'learning_rate': 0.00025,
 'local_batch_size': 1200,
 'local_minibatch_size': 600,
 'local_num_envs': 60,
 'local_rank': 0,
 'max_grad_norm': 0.5,
 'minibatch_size': 600,
 'num_envs': 60,
 'num_minibatches': 2,
 'num_steps': 20,
 'num_updates': 16,
 'profile': False,
 'save_model': True,
 'seed': 1,
 'target_kl': None,
 'test_actor_learner_throughput': False,
 'torch_deterministic': True,
 'total_timesteps': 20000,
 'track': False,
 'upload_model': True,
 'vf_coef': 0.5,
 'wandb_entity': None,
 'wandb_project_name': 'cleanRL',
 'world_size': 1}