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--- |
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library_name: stable-baselines3 |
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tags: |
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- donkey-warren-track-v0 |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- stable-baselines3 |
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model-index: |
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- name: TQC |
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results: |
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- metrics: |
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- type: mean_reward |
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value: 175.85 +/- 2.78 |
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name: mean_reward |
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task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: donkey-warren-track-v0 |
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type: donkey-warren-track-v0 |
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--- |
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# **TQC** Agent playing **donkey-warren-track-v0** |
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This is a trained model of a **TQC** agent playing **donkey-warren-track-v0** |
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) |
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and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). |
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The RL Zoo is a training framework for Stable Baselines3 |
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reinforcement learning agents, |
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with hyperparameter optimization and pre-trained agents included. |
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## Usage (with SB3 RL Zoo) |
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RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> |
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SB3: https://github.com/DLR-RM/stable-baselines3<br/> |
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SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib |
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Autoencoder: https://github.com/araffin/aae-train-donkeycar branch: `feat/race_june` <br/> |
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Gym env: https://github.com/araffin/gym-donkeycar-1 branch: `feat/race_june` <br/> |
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RL Zoo branch: `feat/gym-donkeycar` |
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**Pretrained autoencoder** can be downloaded here: https://github.com/araffin/aae-train-donkeycar/releases/download/live-twitch-2/match_ae-32_monaco_warren.pkl |
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``` |
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export AE_PATH=/path/to/match_ae-32_monaco_warren.pkl |
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# Download model and save it into the logs/ folder |
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python -m rl_zoo3.load_from_hub --algo tqc --env donkey-warren-track-v0 -orga araffin -f logs/ |
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python enjoy.py --algo tqc --env donkey-warren-track-v0 -f logs/ |
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``` |
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## Training (with the RL Zoo) |
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``` |
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python train.py --algo tqc --env donkey-warren-track-v0 -f logs/ |
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# Upload the model and generate video (when possible) |
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python -m rl_zoo3.push_to_hub --algo tqc --env donkey-warren-track-v0 -f logs/ -orga araffin |
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``` |
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## Hyperparameters |
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```python |
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OrderedDict([('batch_size', 256), |
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('buffer_size', 200000), |
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('callback', |
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[{'rl_zoo3.callbacks.ParallelTrainCallback': {'gradient_steps': 200}}, |
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'rl_zoo3.callbacks.LapTimeCallback']), |
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('ent_coef', 'auto'), |
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('env_wrapper', |
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[{'gym.wrappers.time_limit.TimeLimit': {'max_episode_steps': 10000}}, |
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'ae.wrapper.AutoencoderWrapper', |
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{'rl_zoo3.wrappers.HistoryWrapper': {'horizon': 2}}]), |
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('gamma', 0.99), |
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('gradient_steps', 256), |
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('learning_rate', 0.00073), |
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('learning_starts', 500), |
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('n_timesteps', 2000000.0), |
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('normalize', "{'norm_obs': True, 'norm_reward': False}"), |
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('policy', 'MlpPolicy'), |
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('policy_kwargs', |
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'dict(log_std_init=-3, net_arch=[256, 256], n_critics=2, ' |
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'use_expln=True)'), |
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('sde_sample_freq', 16), |
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('tau', 0.02), |
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('train_freq', 200), |
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('use_sde', True), |
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('use_sde_at_warmup', True), |
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('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})]) |
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``` |
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# Environment Arguments |
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```python |
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{'conf': {'cam_resolution': (120, 160, 3), |
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'car_config': {'body_rgb': (226, 112, 18), |
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'body_style': 'donkey', |
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'car_name': 'Toni', |
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'font_size': 40}, |
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'frame_skip': 1, |
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'host': 'localhost', |
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'level': 'warren', |
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'log_level': 20, |
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'max_cte': 8, |
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'port': 9091, |
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'start_delay': 5.0}, |
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'min_throttle': -0.2, |
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'steer': 0.8} |
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``` |
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