TQC Agent playing HopperBulletEnv-v0
This is a trained model of a TQC agent playing HopperBulletEnv-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
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo tqc --env HopperBulletEnv-v0 -orga sb3 -f logs/
python enjoy.py --algo tqc --env HopperBulletEnv-v0 -f logs/
Training (with the RL Zoo)
python train.py --algo tqc --env HopperBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env HopperBulletEnv-v0 -f logs/ -orga sb3
Hyperparameters
OrderedDict([('batch_size', 256),
('buffer_size', 300000),
('ent_coef', 'auto'),
('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
('gamma', 0.98),
('gradient_steps', 64),
('learning_rate', 'lin_7.3e-4'),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(log_std_init=-3, net_arch=[400, 300])'),
('tau', 0.02),
('top_quantiles_to_drop_per_net', 5),
('train_freq', 64),
('use_sde', True),
('normalize', False)])
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Evaluation results
- mean_reward on HopperBulletEnv-v0self-reported2681.19 +/- 26.82