readme: ---
  library_name: stable-baselines3
  tags:
  - MountainCarContinuous-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: TRPO
    results:
    - metrics:
      - type: mean_reward
        value: 92.59 +/- 0.08
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: MountainCarContinuous-v0
        type: MountainCarContinuous-v0
  ---
  
  # **TRPO** Agent playing **MountainCarContinuous-v0**
  This is a trained model of a **TRPO** agent playing **MountainCarContinuous-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 trpo --env MountainCarContinuous-v0 -orga sb3 -f logs/
  python enjoy.py --algo trpo --env MountainCarContinuous-v0  -f logs/
  ```
  
  ## Training (with the RL Zoo)
  ```
  python train.py --algo trpo --env MountainCarContinuous-v0 -f logs/
  # Upload the model and generate video (when possible)
  python -m rl_zoo3.push_to_hub --algo trpo --env MountainCarContinuous-v0 -f logs/ -orga sb3
  ```
  
  ## Hyperparameters
  ```python
  OrderedDict([('n_envs', 2),
               ('n_timesteps', 50000),
               ('normalize', True),
               ('policy', 'MlpPolicy'),
               ('sde_sample_freq', 4),
               ('use_sde', True),
               ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
  ```
  