README.md

metadata

library_name: stable-baselines3
tags:
  - seals/MountainCar-v0
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
model-index:
  - name: PPO
    results:
      - metrics:
          - type: mean_reward
            value: '-100.60 +/- 5.75'
            name: mean_reward
        task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: seals/MountainCar-v0
          type: seals/MountainCar-v0

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 ernestumorga -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 ernestumorga -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 ernestumorga

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', 'dict(norm_obs=False, norm_reward=True)'),
             ('policy',
              'imitation.policies.base.MlpPolicyWithNormalizeFeaturesExtractor'),
             ('policy_kwargs',
              'dict(activation_fn=nn.Tanh, net_arch=[dict(pi=[64, 64], vf=[64, '
              '64])])'),
             ('vf_coef', 0.25988158989488963),
             ('normalize_kwargs', {'norm_obs': False, 'norm_reward': False})])