PPO Agent playing seals/MountainCar-v0

This is a trained model of a PPO agent playing seals/MountainCar-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
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env seals/MountainCar-v0 -orga HumanCompatibleAI -f logs/
python enjoy.py --algo ppo --env seals/MountainCar-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 ppo --env seals/MountainCar-v0 -orga HumanCompatibleAI -f logs/
rl_zoo3 enjoy --algo ppo --env seals/MountainCar-v0 -f logs/


Training (with the RL Zoo)

python train.py --algo ppo --env seals/MountainCar-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/MountainCar-v0 -f logs/ -orga HumanCompatibleAI


Hyperparameters

OrderedDict([('batch_size', 512),
('clip_range', 0.2),
('ent_coef', 6.4940755116195606e-06),
('gae_lambda', 0.98),
('gamma', 0.99),
('learning_rate', 0.0004476103728105138),
('max_grad_norm', 1),
('n_envs', 16),
('n_epochs', 20),
('n_steps', 256),
('n_timesteps', 1000000.0),
('normalize',
{'gamma': 0.99, 'norm_obs': False, 'norm_reward': True}),
('policy', 'MlpPolicy'),
('policy_kwargs',
{'activation_fn': <class 'torch.nn.modules.activation.Tanh'>,
'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
('vf_coef', 0.25988158989488963),
('normalize_kwargs',
{'norm_obs': {'gamma': 0.99,
'norm_obs': False,
'norm_reward': True},
'norm_reward': False})])