PPO Agent playing Pendulum-v1
This is a trained model of a PPO agent playing Pendulum-v1 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 ppo --env Pendulum-v1 -orga HumanCompatibleAI -f logs/
python -m rl_zoo3.enjoy --algo ppo --env Pendulum-v1 -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 Pendulum-v1 -orga HumanCompatibleAI -f logs/
python -m rl_zoo3.enjoy --algo ppo --env Pendulum-v1 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo ppo --env Pendulum-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env Pendulum-v1 -f logs/ -orga HumanCompatibleAI
Hyperparameters
OrderedDict([('clip_range', 0.2),
('ent_coef', 0.0),
('gae_lambda', 0.95),
('gamma', 0.9),
('learning_rate', 0.001),
('n_envs', 4),
('n_epochs', 10),
('n_steps', 1024),
('n_timesteps', 100000.0),
('policy', 'MlpPolicy'),
('sde_sample_freq', 4),
('use_sde', True),
('normalize', False)])
Environment Arguments
{'render_mode': 'rgb_array'}