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tqc-Walker2d-v3
sb3/tqc-Walker2d-v3
tqc-Walker2d-v3 is a reinforcement learning model from sb3. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3.
This is a trained model of a TQC agent playing Walker2d-v3 using the stable-baselines3 library and the RL Zoo.
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At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- Jun 2, 2022
- Updated
- Oct 11, 2022
- SHA
- 00aff9fc
Model card
From the Hugging Face model README
TQC Agent playing Walker2d-v3
This is a trained model of a TQC agent playing Walker2d-v3 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<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> 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 Walker2d-v3 -orga sb3 -f logs/
python enjoy.py --algo tqc --env Walker2d-v3 -f logs/
Training (with the RL Zoo)
python train.py --algo tqc --env Walker2d-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env Walker2d-v3 -f logs/ -orga sb3
Hyperparameters
OrderedDict([('learning_starts', 10000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('use_sde', False),
('normalize', False)])
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Jun 2, 2022
- Updated
- Oct 11, 2022