readme: ---
  library_name: stable-baselines3
  tags:
  - Walker2DBulletEnv-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: TD3
    results:
    - metrics:
      - type: mean_reward
        value: 2240.34 +/- 19.52
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: Walker2DBulletEnv-v0
        type: Walker2DBulletEnv-v0
  ---
  
  # **TD3** Agent playing **Walker2DBulletEnv-v0**
  This is a trained model of a **TD3** agent playing **Walker2DBulletEnv-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 td3 --env Walker2DBulletEnv-v0 -orga sb3 -f logs/
  python enjoy.py --algo td3 --env Walker2DBulletEnv-v0  -f logs/
  ```
  
  ## Training (with the RL Zoo)
  ```
  python train.py --algo td3 --env Walker2DBulletEnv-v0 -f logs/
  # Upload the model and generate video (when possible)
  python -m rl_zoo3.push_to_hub --algo td3 --env Walker2DBulletEnv-v0 -f logs/ -orga sb3
  ```
  
  ## Hyperparameters
  ```python
  OrderedDict([('buffer_size', 200000),
               ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
               ('gamma', 0.98),
               ('gradient_steps', -1),
               ('learning_rate', 0.001),
               ('learning_starts', 10000),
               ('n_timesteps', 1000000.0),
               ('noise_std', 0.1),
               ('noise_type', 'normal'),
               ('policy', 'MlpPolicy'),
               ('policy_kwargs', 'dict(net_arch=[400, 300])'),
               ('train_freq', [1, 'episode']),
               ('normalize', False)])
  ```
  