TQC Agent playing FetchSlide-v1
This is a trained model of a TQC agent playing FetchSlide-v1 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 FetchSlide-v1 -orga sb3 -f logs/
python enjoy.py --algo tqc --env FetchSlide-v1 -f logs/
Training (with the RL Zoo)
python train.py --algo tqc --env FetchSlide-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env FetchSlide-v1 -f logs/ -orga sb3
Hyperparameters
OrderedDict([('batch_size', 512),
('buffer_size', 1000000),
('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
('gamma', 0.98),
('learning_rate', 0.001),
('n_timesteps', 3000000.0),
('policy', 'MultiInputPolicy'),
('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
('replay_buffer_class', 'HerReplayBuffer'),
('replay_buffer_kwargs',
"dict( online_sampling=True, goal_selection_strategy='future', "
'n_sampled_goal=4, max_episode_length=100 )'),
('tau', 0.005),
('normalize', False)])
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Evaluation results
- mean_reward on FetchSlide-v1self-reported-29.00 +/- 9.66