PPO Agent playing AsteroidsNoFrameskip-v4
This is a trained model of a PPO agent playing AsteroidsNoFrameskip-v4 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 AsteroidsNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo ppo --env AsteroidsNoFrameskip-v4 -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 AsteroidsNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo ppo --env AsteroidsNoFrameskip-v4 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo ppo --env AsteroidsNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env AsteroidsNoFrameskip-v4 -f logs/ -orga MattStammers
Hyperparameters
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 10000000.0),
('normalize', False),
('policy', 'CnnPolicy'),
('vf_coef', 0.5)])
Environment Arguments
{'render_mode': 'rgb_array'}
Annoyingly the asteroids are not rendered in the video (at the moment either the asteroids or the ship is rendering) but you can see that he his pretty active in pursuing them.
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Evaluation results
- mean_reward on AsteroidsNoFrameskip-v4self-reported1824.00 +/- 644.72