readme: ---
  library_name: stable-baselines3
  tags:
  - donkey-minimonaco-track-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: TQC
    results:
    - metrics:
      - type: mean_reward
        value: 386.49 +/- 0.77
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: donkey-minimonaco-track-v0
        type: donkey-minimonaco-track-v0
  ---
  
  # **TQC** Agent playing **donkey-minimonaco-track-v0**
  This is a trained model of a **TQC** agent playing **donkey-minimonaco-track-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
  
  Autoencoder: https://github.com/araffin/aae-train-donkeycar branch: `feat/race_june` <br/>
  Gym env: https://github.com/araffin/gym-donkeycar-1 branch: `feat/race_june` <br/>
  RL Zoo branch: `feat/gym-donkeycar`
  
  **Pretrained autoencoder** can be downloaded here: https://github.com/araffin/aae-train-donkeycar/releases/download/live-twitch-2/ae-32_monaco.pkl
  
  
  ```
  export AE_PATH=/path/to/ae-32_monaco.pkl
  # Download model and save it into the logs/ folder
  python -m rl_zoo3.load_from_hub --algo tqc --env donkey-minimonaco-track-v0 -orga araffin -f logs/
  python enjoy.py --algo tqc --env donkey-minimonaco-track-v0  -f logs/
  ```
  
  ## Training (with the RL Zoo)
  ```
  python train.py --algo tqc --env donkey-minimonaco-track-v0 -f logs/
  # Upload the model and generate video (when possible)
  python -m rl_zoo3.push_to_hub --algo tqc --env donkey-minimonaco-track-v0 -f logs/ -orga araffin
  ```
  
  ## Hyperparameters
  ```python
  OrderedDict([('batch_size', 256),
               ('buffer_size', 200000),
               ('callback',
                [{'rl_zoo3.callbacks.ParallelTrainCallback': {'gradient_steps': 200}},
                 'rl_zoo3.callbacks.LapTimeCallback']),
               ('ent_coef', 'auto'),
               ('env_wrapper',
                [{'gym.wrappers.time_limit.TimeLimit': {'max_episode_steps': 10000}},
                 'ae.wrapper.AutoencoderWrapper',
                 {'rl_zoo3.wrappers.HistoryWrapper': {'horizon': 2}}]),
               ('gamma', 0.99),
               ('gradient_steps', 256),
               ('learning_rate', 0.00073),
               ('learning_starts', 500),
               ('n_timesteps', 2000000.0),
               ('normalize', "{'norm_obs': True, 'norm_reward': False}"),
               ('policy', 'MlpPolicy'),
               ('policy_kwargs',
                'dict(log_std_init=-3, net_arch=[256, 256], n_critics=2, '
                'use_expln=True)'),
               ('sde_sample_freq', 16),
               ('tau', 0.02),
               ('train_freq', 200),
               ('use_sde', True),
               ('use_sde_at_warmup', True),
               ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
  ```
  
  # Environment Arguments
  ```python
  {'conf': {'cam_resolution': (120, 160, 3),
            'car_config': {'body_rgb': (226, 112, 18),
                           'body_style': 'donkey',
                           'car_name': 'Toni',
                           'font_size': 40},
            'frame_skip': 1,
            'host': 'localhost',
            'level': 'mini_monaco',
            'log_level': 20,
            'max_cte': 8,
            'port': 9091,
            'start_delay': 5.0},
   'min_throttle': -0.2,
   'steer': 0.8}
  ```
  