readme: ---
  library_name: stable-baselines3
  tags:
  - CartpoleDMC-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: DDPG
    results:
    - task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: CartpoleDMC-v0
        type: CartpoleDMC-v0
      metrics:
      - type: mean_reward
        value: 885.50 +/- 4.94
        name: mean_reward
        verified: false
  ---
  
  # **DDPG** Agent playing **CartpoleDMC-v0**
  This is a trained model of a **DDPG** agent playing **CartpoleDMC-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
  
  Install the RL Zoo (with SB3 and SB3-Contrib):
  ```bash
  pip install rl_zoo3
  ```
  
  ```
  # Download model and save it into the logs/ folder
  python -m rl_zoo3.load_from_hub --algo ddpg --env CartpoleDMC-v0 -orga qgallouedec -f logs/
  python -m rl_zoo3.enjoy --algo ddpg --env CartpoleDMC-v0  -f logs/
  ```
  
  If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
  ```
  python -m rl_zoo3.load_from_hub --algo ddpg --env CartpoleDMC-v0 -orga qgallouedec -f logs/
  python -m rl_zoo3.enjoy --algo ddpg --env CartpoleDMC-v0  -f logs/
  ```
  
  ## Training (with the RL Zoo)
  ```
  python -m rl_zoo3.train --algo ddpg --env CartpoleDMC-v0 -f logs/
  # Upload the model and generate video (when possible)
  python -m rl_zoo3.push_to_hub --algo ddpg --env CartpoleDMC-v0 -f logs/ -orga qgallouedec
  ```
  
  ## Hyperparameters
  ```python
  OrderedDict([('batch_size', 64),
               ('gamma', 0.99),
               ('learning_rate', 0.0001),
               ('n_timesteps', 1000000.0),
               ('noise_std', 0.3),
               ('noise_type', 'ornstein-uhlenbeck'),
               ('policy', 'MlpPolicy'),
               ('policy_kwargs',
                'dict(net_arch=dict(pi=[300, 200], qf=[400, 300]))'),
               ('normalize', False)])
  ```
  