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araffin/
tqc-donkey-avc-sparkfun-v0

Name: tqc-donkey-avc-sparkfun-v0
Availability: InStock

stable-baselines3

donkey-avc-sparkfun-v0

deep-reinforcement-learning

reinforcement-learning

model-index

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TQC Agent playing donkey-avc-sparkfun-v0

This is a trained model of a TQC agent playing donkey-avc-sparkfun-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/ae-32_avc.pkl

# Export path to autoencoder
export AE_PATH=/path/to/ae-32_avc.pkl
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo tqc --env donkey-avc-sparkfun-v0 -orga araffin -f logs/
python enjoy.py --algo tqc --env donkey-avc-sparkfun-v0  -f logs/

Training (with the RL Zoo)

python train.py --algo tqc --env donkey-avc-sparkfun-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env donkey-avc-sparkfun-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',
              ['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