features_extractor_kwargs: Optional[Dict[str, Any]] = None, normalize_images: bool = True, optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam, optimizer_kwargs: Optional[Dict[str, Any]] = None, n_critics: int = 2, share_features_extractor: bool = True, ): super(CnnPolicy, self).__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, use_sde, log_std_init, sde_net_arch, use_expln, clip_mean, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, n_critics, share_features_extractor, ) register_policy("SAC", "MlpPolicy", MlpPolicy) register_policy("SAC", "CnnPolicy", CnnPolicy)
""" def __init__( self, observation_space: spaces.Space, action_space: spaces.Space, lr_schedule: Schedule, net_arch: Optional[List[int]] = None, activation_fn: Type[nn.Module] = nn.ReLU, features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN, features_extractor_kwargs: Optional[Dict[str, Any]] = None, normalize_images: bool = True, optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam, optimizer_kwargs: Optional[Dict[str, Any]] = None, ): super(CnnPolicy, self).__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, ) register_policy("DQN", "MlpPolicy", MlpPolicy) register_policy("DQN", "CnnPolicy", CnnPolicy)
# This file is here just to define MlpPolicy/CnnPolicy # that work for PPO from hmlf.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy from hmlf.common.policy_register import register_policy MlpPolicy = ActorCriticPolicy CnnPolicy = ActorCriticCnnPolicy register_policy("PPO", "MlpPolicy", MlpPolicy) register_policy("PPO", "CnnPolicy", CnnPolicy)
self, observation_space: Space, action_space: Space, lr_schedule: Schedule, net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None, activation_fn: Type[nn.Module] = nn.ReLU, features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN, features_extractor_kwargs: Optional[Dict[str, Any]] = None, normalize_images: bool = True, optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam, optimizer_kwargs: Optional[Dict[str, Any]] = None, share_features_extractor: bool = True, ): super().__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, share_features_extractor, ) register_policy("SDDPG", "MlpPolicy", MlpPolicy) register_policy("SDDPG", "CnnPolicy", CnnPolicy)
action_space: spaces.Space, lr_schedule: Schedule, net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None, activation_fn: Type[nn.Module] = nn.ReLU, features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN, features_extractor_kwargs: Optional[Dict[str, Any]] = None, normalize_images: bool = True, optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam, optimizer_kwargs: Optional[Dict[str, Any]] = None, n_critics: int = 2, share_features_extractor: bool = True, ): super(CnnPolicy, self).__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, n_critics, share_features_extractor, ) register_policy("TD3", "MlpPolicy", MlpPolicy) register_policy("TD3", "CnnPolicy", CnnPolicy)