readme: ---
  tags:
  - TimePilot-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: TimePilot-v5
        type: TimePilot-v5
      metrics:
      - type: mean_reward
        value: 37610.00 +/- 9766.72
        name: mean_reward
        verified: false
  ---
  
  # (CleanRL) **PPO** Agent Playing **TimePilot-v5**
  
  This is a trained model of a PPO agent playing TimePilot-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_async_jax_scan_impalanet_machado.py).
  
  ## Get Started
  
  To use this model, please install the `cleanrl` package with the following command:
  
  ```
  pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
  python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id TimePilot-v5
  ```
  
  Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
  
  
  ## Command to reproduce the training
  
  ```bash
  curl -OL https://huggingface.co/cleanrl/TimePilot-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
  curl -OL https://huggingface.co/cleanrl/TimePilot-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
  curl -OL https://huggingface.co/cleanrl/TimePilot-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
  poetry install --all-extras
  python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id TimePilot-v5 --seed 1
  ```
  
  # Hyperparameters
  ```python
  {'anneal_lr': True,
   'async_batch_size': 16,
   'batch_size': 2048,
   'capture_video': False,
   'clip_coef': 0.1,
   'cuda': True,
   'ent_coef': 0.01,
   'env_id': 'TimePilot-v5',
   'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
   'gae': True,
   'gae_lambda': 0.95,
   'gamma': 0.99,
   'hf_entity': 'cleanrl',
   'learning_rate': 0.00025,
   'max_grad_norm': 0.5,
   'minibatch_size': 1024,
   'norm_adv': True,
   'num_envs': 64,
   'num_minibatches': 2,
   'num_steps': 32,
   'num_updates': 24414,
   'save_model': True,
   'seed': 1,
   'target_kl': None,
   'torch_deterministic': True,
   'total_timesteps': 50000000,
   'track': True,
   'update_epochs': 2,
   'upload_model': True,
   'vf_coef': 0.5,
   'wandb_entity': None,
   'wandb_project_name': 'envpool-atari'}
  ```
      