PPO Agent playing seals/Walker2d-v1
This is a trained model of a PPO agent playing seals/Walker2d-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 seals/Walker2d-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Walker2d-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 seals/Walker2d-v1 -orga ernestum -f logs/
python -m rl_zoo3.enjoy --algo ppo --env seals/Walker2d-v1 -f logs/
Training (with the RL Zoo)
python -m rl_zoo3.train --algo ppo --env seals/Walker2d-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/Walker2d-v1 -f logs/ -orga ernestum
Hyperparameters
OrderedDict([('batch_size', 8),
('clip_range', 0.4),
('ent_coef', 0.00013057334805552262),
('gae_lambda', 0.92),
('gamma', 0.98),
('learning_rate', 3.791707778339674e-05),
('max_grad_norm', 0.6),
('n_envs', 1),
('n_epochs', 5),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize',
{'gamma': 0.98, 'norm_obs': False, 'norm_reward': True}),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.ReLU'>,
'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
'net_arch': [{'pi': [256, 256], 'vf': [256, 256]}]}),
('vf_coef', 0.6167177795726859),
('normalize_kwargs',
{'norm_obs': {'gamma': 0.98,
'norm_obs': False,
'norm_reward': True},
'norm_reward': False})])
Environment Arguments
{'render_mode': 'rgb_array'}
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Evaluation results
- mean_reward on seals/Walker2d-v1self-reported2465.56 +/- 272.31