模型:
HumanCompatibleAI/ppo-seals-MountainCar-v0
这是一个经过训练的PPO智能体模型,使用 stable-baselines3 library 和 RL Zoo 。
RL Zoo是一个训练框架,适用于Stable Baselines3强化学习智能体,包括超参数优化和预训练智能体。
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/
如果通过pip安装了RL Zoo3(pip install rl_zoo3),则可以在任何地方执行以下操作:
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/
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
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})])