library_name: stable-baselines3
tags:
- donkey-warren-track-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- metrics:
- type: mean_reward
value: 175.85 +/- 2.78
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: donkey-warren-track-v0
type: donkey-warren-track-v0
TQC Agent playing donkey-warren-track-v0
This is a trained model of a TQC agent playing donkey-warren-track-v0 using the stable-baselines3 library and the RL 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
SB3: https://github.com/DLR-RM/stable-baselines3
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Autoencoder: https://github.com/araffin/aae-train-donkeycar branch: feat/race_june
Gym env: https://github.com/araffin/gym-donkeycar-1 branch: feat/race_june
RL Zoo branch: feat/gym-donkeycar
Pretrained autoencoder can be downloaded here: https://github.com/araffin/aae-train-donkeycar/releases/download/live-twitch-2/match_ae-32_monaco_warren.pkl
export AE_PATH=/path/to/match_ae-32_monaco_warren.pkl
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo tqc --env donkey-warren-track-v0 -orga araffin -f logs/
python enjoy.py --algo tqc --env donkey-warren-track-v0 -f logs/
Training (with the RL Zoo)
python train.py --algo tqc --env donkey-warren-track-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env donkey-warren-track-v0 -f logs/ -orga araffin
Hyperparameters
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
{'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': 'warren',
'log_level': 20,
'max_cte': 8,
'port': 9091,
'start_delay': 5.0},
'min_throttle': -0.2,
'steer': 0.8}