readme: ---
  library_name: stable-baselines3
  tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: RecurrentPPO
    results:
    - metrics:
      - type: mean_reward
        value: 282.21 +/- 11.78
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: LunarLander-v2
        type: LunarLander-v2
  ---
  
  # **RecurrentPPO** Agent playing **LunarLander-v2**
  This is a trained model of a **RecurrentPPO** agent playing **LunarLander-v2**
  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 utils.load_from_hub --algo ppo_lstm --env LunarLander-v2 -orga Corianas -f logs/
  python enjoy.py --algo ppo_lstm --env LunarLander-v2  -f logs/
  ```
  
  ## Training (with the RL Zoo)
  ```
  python train.py --algo ppo_lstm --env LunarLander-v2 -f logs/
  # Upload the model and generate video (when possible)
  python -m utils.push_to_hub --algo ppo_lstm --env LunarLander-v2 -f logs/ -orga Corianas
  ```
  
  ## Hyperparameters
  ```python
  OrderedDict([('batch_size', 128),
               ('ent_coef', 0.01),
               ('gae_lambda', 0.98),
               ('gamma', 0.999),
               ('n_envs', 8),
               ('n_epochs', 4),
               ('n_steps', 512),
               ('n_timesteps', 5000000.0),
               ('normalize', True),
               ('policy', 'MlpLstmPolicy'),
               ('policy_kwargs',
                'dict( ortho_init=False, activation_fn=nn.ReLU, '
                'lstm_hidden_size=64, enable_critic_lstm=True, '
                'net_arch=[dict(pi=[64], vf=[64])] )'),
               ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
  ```
  