metadata
library_name: stable-baselines3
tags:
- Walker2d-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Walker2d-v3
type: Walker2d-v3
metrics:
- type: mean_reward
value: 4037.03 +/- 50.65
name: mean_reward
verified: false
TD3 Agent playing Walker2d-v3
This is a trained model of a TD3 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
SB3: https://github.com/DLR-RM/stable-baselines3
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
pip install rl_zoo3
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo td3 --env Walker2d-v3 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env Walker2d-v3 -f logs/
If you installed the RL Zoo3 via pip (pip install rl_zoo3
), from anywhere you can do:
python -m rl_zoo3.load_from_hub --algo td3 --env Walker2d-v3 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env Walker2d-v3 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo td3 --env Walker2d-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo td3 --env Walker2d-v3 -f logs/ -orga qgallouedec
Hyperparameters
OrderedDict([('learning_starts', 10000),
('n_timesteps', 1000000.0),
('noise_std', 0.1),
('noise_type', 'normal'),
('policy', 'MlpPolicy'),
('normalize', False)])