A2C Agent playing HopperBulletEnv-v0
This is a trained model of a A2C agent playing HopperBulletEnv-v0 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 a2c --env HopperBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env HopperBulletEnv-v0 -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 a2c --env HopperBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env HopperBulletEnv-v0 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo a2c --env HopperBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env HopperBulletEnv-v0 -f logs/ -orga qgallouedec
Hyperparameters
OrderedDict([('ent_coef', 0.0),
('gae_lambda', 0.9),
('gamma', 0.99),
('learning_rate', 'lin_0.00096'),
('max_grad_norm', 0.5),
('n_envs', 4),
('n_steps', 8),
('n_timesteps', 2000000.0),
('normalize', True),
('normalize_advantage', False),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'),
('use_rms_prop', True),
('use_sde', True),
('vf_coef', 0.4),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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Evaluation results
- mean_reward on HopperBulletEnv-v0self-reported360.05 +/- 486.80