ppo-door-lock-v2

PPO Agent playing door-lock-v2

This is a trained model of a PPO agent playing door-lock-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 ppo --env door-lock-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ppo --env door-lock-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 ppo --env door-lock-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ppo --env door-lock-v2  -f logs/

Training (with the RL Zoo)

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

Hyperparameters

OrderedDict([('batch_size', 32),
             ('gamma', 0.99),
             ('learning_rate', 0.0005),
             ('n_envs', 4),
             ('n_steps', 512),
             ('n_timesteps', 10000000),
             ('normalize', True),
             ('policy', 'MlpPolicy'),
             ('target_kl', 0.04),
             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])