ppo_lstm-MountainCarContinuousNoVel-v0

RecurrentPPO Agent playing MountainCarContinuousNoVel-v0

This is a trained model of a RecurrentPPO agent playing MountainCarContinuousNoVel-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 rl_zoo3.load_from_hub --algo ppo_lstm --env MountainCarContinuousNoVel-v0 -orga sb3 -f logs/
python enjoy.py --algo ppo_lstm --env MountainCarContinuousNoVel-v0  -f logs/

Training (with the RL Zoo)

python train.py --algo ppo_lstm --env MountainCarContinuousNoVel-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo_lstm --env MountainCarContinuousNoVel-v0 -f logs/ -orga sb3

Hyperparameters

OrderedDict([('batch_size', 256),
             ('clip_range', 0.1),
             ('ent_coef', 0.00429),
             ('gae_lambda', 0.9),
             ('gamma', 0.9999),
             ('learning_rate', 7.77e-05),
             ('max_grad_norm', 5),
             ('n_envs', 8),
             ('n_epochs', 10),
             ('n_steps', 1024),
             ('n_timesteps', 300000.0),
             ('normalize', True),
             ('policy', 'MlpLstmPolicy'),
             ('policy_kwargs',
              'dict(log_std_init=0.0, ortho_init=False, lstm_hidden_size=32, '
              'enable_critic_lstm=True, net_arch=[dict(pi=[64], vf=[64])])'),
             ('sde_sample_freq', 8),
             ('use_sde', True),
             ('vf_coef', 0.19),
             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])