readme: ---
  library_name: stable-baselines3
  tags:
  - Hopper-v3
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: PPO
    results:
    - metrics:
      - type: mean_reward
        value: 2410.11 +/- 9.86
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: Hopper-v3
        type: Hopper-v3
  ---
  
  # **PPO** Agent playing **Hopper-v3**
  This is a trained model of a **PPO** agent playing **Hopper-v3**
  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 --env Hopper-v3 -orga sb3 -f logs/
  python enjoy.py --algo ppo --env Hopper-v3  -f logs/
  ```
  
  ## Training (with the RL Zoo)
  ```
  python train.py --algo ppo --env Hopper-v3 -f logs/
  # Upload the model and generate video (when possible)
  python -m rl_zoo3.push_to_hub --algo ppo --env Hopper-v3 -f logs/ -orga sb3
  ```
  
  ## Hyperparameters
  ```python
  OrderedDict([('batch_size', 32),
               ('clip_range', 0.2),
               ('ent_coef', 0.00229519),
               ('gae_lambda', 0.99),
               ('gamma', 0.999),
               ('learning_rate', 9.80828e-05),
               ('max_grad_norm', 0.7),
               ('n_envs', 1),
               ('n_epochs', 5),
               ('n_steps', 512),
               ('n_timesteps', 1000000.0),
               ('normalize', True),
               ('policy', 'MlpPolicy'),
               ('policy_kwargs',
                'dict( log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, '
                'net_arch=[dict(pi=[256, 256], vf=[256, 256])] )'),
               ('vf_coef', 0.835671),
               ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
  ```
  