readme: ---
  library_name: stable-baselines3
  tags:
  - CarRacing-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: RecurrentPPO
    results:
    - metrics:
      - type: mean_reward
        value: 880.39 +/- 31.90
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: CarRacing-v0
        type: CarRacing-v0
  ---
  
  # **RecurrentPPO** Agent playing **CarRacing-v0**
  This is a trained model of a **RecurrentPPO** agent playing **CarRacing-v0**
  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
  and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
  
  The RL Zoo is a training framework for Stable Baselines3
  reinforcement learning agents,
  with hyperparameter optimization and pre-trained agents included.
  
  ## Usage (with SB3 RL Zoo)
  
  RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
  SB3: https://github.com/DLR-RM/stable-baselines3<br/>
  SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
  
  ```
  # Download model and save it into the logs/ folder
  python -m rl_zoo3.load_from_hub --algo ppo_lstm --env CarRacing-v0 -orga sb3 -f logs/
  python enjoy.py --algo ppo_lstm --env CarRacing-v0  -f logs/
  ```
  
  ## Training (with the RL Zoo)
  ```
  python train.py --algo ppo_lstm --env CarRacing-v0 -f logs/
  # Upload the model and generate video (when possible)
  python -m rl_zoo3.push_to_hub --algo ppo_lstm --env CarRacing-v0 -f logs/ -orga sb3
  ```
  
  ## Hyperparameters
  ```python
  OrderedDict([('batch_size', 128),
               ('clip_range', 0.2),
               ('ent_coef', 0.0),
               ('env_wrapper',
                [{'gym.wrappers.resize_observation.ResizeObservation': {'shape': 64}},
                 {'gym.wrappers.gray_scale_observation.GrayScaleObservation': {'keep_dim': True}}]),
               ('frame_stack', 2),
               ('gae_lambda', 0.95),
               ('gamma', 0.99),
               ('learning_rate', 'lin_1e-4'),
               ('max_grad_norm', 0.5),
               ('n_envs', 8),
               ('n_epochs', 10),
               ('n_steps', 512),
               ('n_timesteps', 4000000.0),
               ('normalize', "{'norm_obs': False, 'norm_reward': True}"),
               ('policy', 'CnnLstmPolicy'),
               ('policy_kwargs',
                'dict(log_std_init=-2, ortho_init=False, '
                'enable_critic_lstm=False, activation_fn=nn.GELU, '
                'lstm_hidden_size=128, )'),
               ('sde_sample_freq', 4),
               ('use_sde', True),
               ('vf_coef', 0.5),
               ('normalize_kwargs', {'norm_obs': False, 'norm_reward': False})])
  ```
  
  # Environment Arguments
  ```python
  {'verbose': 0}
  ```
  