sac-assembly-v2

SAC Agent playing assembly-v2

This is a trained model of a SAC agent playing assembly-v2 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 sac --env assembly-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo sac --env assembly-v2  -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 sac --env assembly-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo sac --env assembly-v2  -f logs/

Training (with the RL Zoo)

python -m rl_zoo3.train --algo sac --env assembly-v2 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env assembly-v2 -f logs/ -orga qgallouedec

Hyperparameters

OrderedDict([('batch_size', 500),
             ('buffer_size', 500000),
             ('ent_coef', 'auto'),
             ('gamma', 0.99),
             ('gradient_steps', -1),
             ('learning_rate', 0.0003),
             ('learning_starts', 10000),
             ('n_timesteps', 1000000.0),
             ('policy', 'MlpPolicy'),
             ('policy_kwargs', 'dict(log_std_init=-3, net_arch=[256, 256])'),
             ('tau', 0.005),
             ('train_freq', [1, 'episode']),
             ('normalize', False)])