PPO Agent playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-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 CartPole-v1 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env CartPole-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 CartPole-v1 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env CartPole-v1 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo ppo --env CartPole-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env CartPole-v1 -f logs/ -orga sb3
Hyperparameters
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.2'),
('ent_coef', 0.0),
('gae_lambda', 0.8),
('gamma', 0.98),
('learning_rate', 'lin_0.001'),
('n_envs', 8),
('n_epochs', 20),
('n_steps', 32),
('n_timesteps', 100000.0),
('policy', 'MlpPolicy'),
('normalize', False)])
Environment Arguments
{'render_mode': 'rgb_array'}