(CleanRL) PPO Agent Playing Pong-v5
This is a trained model of a PPO agent playing Pong-v5. The model was trained by using CleanRL and the most up-to-date training code can be found here.
Command to reproduce the training
curl -OL https://huggingface.co/vwxyzjn/Pong-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/ppo_atari_envpool_xla_jax_scan.py
curl -OL https://huggingface.co/vwxyzjn/Pong-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/vwxyzjn/Pong-v5-ppo_atari_envpool_xla_jax_scan-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_xla_jax_scan.py --save-model --total-timesteps 1025 --upload-model
Hyperparameters
{'anneal_lr': True,
'batch_size': 1024,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Pong-v5',
'exp_name': 'ppo_atari_envpool_xla_jax_scan',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': '',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 256,
'norm_adv': True,
'num_envs': 8,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 1,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 1025,
'track': False,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}