PPO Agent playing Hopper-v3
This is a trained model of a PPO agent playing Hopper-v3 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
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env Hopper-v3 -orga sb3 -f logs/
python enjoy.py --algo ppo --env Hopper-v3 -f logs/
Training (with the RL Zoo)
python train.py --algo ppo --env Hopper-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env Hopper-v3 -f logs/ -orga sb3
Hyperparameters
OrderedDict([('batch_size', 32),
('clip_range', 0.2),
('ent_coef', 0.00229519),
('gae_lambda', 0.99),
('gamma', 0.999),
('learning_rate', 9.80828e-05),
('max_grad_norm', 0.7),
('n_envs', 1),
('n_epochs', 5),
('n_steps', 512),
('n_timesteps', 1000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict( log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, '
'net_arch=[dict(pi=[256, 256], vf=[256, 256])] )'),
('vf_coef', 0.835671),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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