tqc-ReachCube-v0

TQC Agent playing ReachCube-v0

This is a trained model of a TQC agent playing ReachCube-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<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> 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 tqc --env ReachCube-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env ReachCube-v0  -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 tqc --env ReachCube-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env ReachCube-v0  -f logs/

Training (with the RL Zoo)

python -m rl_zoo3.train --algo tqc --env ReachCube-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env ReachCube-v0 -f logs/ -orga qgallouedec

Hyperparameters

OrderedDict([('n_envs', 16),
             ('n_timesteps', 10000000.0),
             ('policy', 'MultiInputPolicy'),
             ('use_sde', True),
             ('normalize', False)])

Environment Arguments

{'observation_mode': 'state', 'render_mode': 'rgb_array'}