readme: ---
  tags:
  - Pong-v5
  - deep-reinforcement-learning
  - reinforcement-learning
  - custom-implementation
  library_name: cleanrl
  model-index:
  - name: PPO
    results:
    - task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: Pong-v5
        type: Pong-v5
      metrics:
      - type: mean_reward
        value: -20.30 +/- 0.78
        name: mean_reward
        verified: false
  ---
  
  # (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](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
  found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_xla_jax_scan.py).
  
  ## Command to reproduce the training
  
  ```bash
  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
  ```python
  {'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'}
  ```
      