ppo-seals-HalfCheetah-v0

PPO Agent playing seals/HalfCheetah-v0

This is a trained model of a PPO agent playing seals/HalfCheetah-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<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo ppo --env seals/HalfCheetah-v0 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env seals/HalfCheetah-v0  -f logs/

Training (with the RL Zoo)

python train.py --algo ppo --env seals/HalfCheetah-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env seals/HalfCheetah-v0 -f logs/ -orga ernestumorga

Hyperparameters

OrderedDict([('batch_size', 64),
             ('clip_range', 0.1),
             ('ent_coef', 3.794797423594763e-06),
             ('gae_lambda', 0.95),
             ('gamma', 0.95),
             ('learning_rate', 0.0003286871805949382),
             ('max_grad_norm', 0.8),
             ('n_envs', 1),
             ('n_epochs', 5),
             ('n_steps', 512),
             ('n_timesteps', 1000000.0),
             ('normalize', True),
             ('policy', 'MlpPolicy'),
             ('policy_kwargs',
              'dict(activation_fn=nn.Tanh, net_arch=[dict(pi=[64, 64], vf=[64, '
              '64])])'),
             ('vf_coef', 0.11483689492120866),
             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])