238 lines
8.4 KiB
Python
238 lines
8.4 KiB
Python
from typing import Any, Dict, List, Optional, Type
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import gym
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import torch as th
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from torch import nn
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from stable_baselines3.common.policies import BasePolicy, register_policy
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from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
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from stable_baselines3.common.type_aliases import Schedule
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class QNetwork(BasePolicy):
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"""
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Action-Value (Q-Value) network for DQN
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:param observation_space: Observation space
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:param action_space: Action space
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:param net_arch: The specification of the policy and value networks.
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:param activation_fn: Activation function
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:param normalize_images: Whether to normalize images or not,
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dividing by 255.0 (True by default)
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"""
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def __init__(
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self,
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observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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features_extractor: nn.Module,
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features_dim: int,
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net_arch: Optional[List[int]] = None,
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activation_fn: Type[nn.Module] = nn.ReLU,
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normalize_images: bool = True,
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):
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super(QNetwork, self).__init__(
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observation_space,
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action_space,
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features_extractor=features_extractor,
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normalize_images=normalize_images,
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)
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if net_arch is None:
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net_arch = [64, 64]
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self.net_arch = net_arch
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self.activation_fn = activation_fn
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self.features_extractor = features_extractor
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self.features_dim = features_dim
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self.normalize_images = normalize_images
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action_dim = self.action_space.n # number of actions
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q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)
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self.q_net = nn.Sequential(*q_net)
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def forward(self, obs: th.Tensor) -> th.Tensor:
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"""
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Predict the q-values.
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:param obs: Observation
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:return: The estimated Q-Value for each action.
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"""
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return self.q_net(self.extract_features(obs))
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def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
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q_values = self.forward(observation)
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# Greedy action
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action = q_values.argmax(dim=1).reshape(-1)
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return action
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def _get_constructor_parameters(self) -> Dict[str, Any]:
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data = super()._get_constructor_parameters()
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data.update(
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dict(
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net_arch=self.net_arch,
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features_dim=self.features_dim,
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activation_fn=self.activation_fn,
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features_extractor=self.features_extractor,
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)
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)
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return data
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class DQNPolicy(BasePolicy):
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"""
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Policy class with Q-Value Net and target net for DQN
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:param observation_space: Observation space
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:param action_space: Action space
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:param lr_schedule: Learning rate schedule (could be constant)
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:param net_arch: The specification of the policy and value networks.
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:param activation_fn: Activation function
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:param features_extractor_class: Features extractor to use.
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:param features_extractor_kwargs: Keyword arguments
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to pass to the features extractor.
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:param normalize_images: Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: Additional keyword arguments,
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excluding the learning rate, to pass to the optimizer
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"""
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def __init__(
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self,
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observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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lr_schedule: Schedule,
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net_arch: Optional[List[int]] = None,
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activation_fn: Type[nn.Module] = nn.ReLU,
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features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
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features_extractor_kwargs: Optional[Dict[str, Any]] = None,
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normalize_images: bool = True,
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optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
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optimizer_kwargs: Optional[Dict[str, Any]] = None,
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):
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super(DQNPolicy, self).__init__(
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observation_space,
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action_space,
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features_extractor_class,
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features_extractor_kwargs,
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optimizer_class=optimizer_class,
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optimizer_kwargs=optimizer_kwargs,
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)
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if net_arch is None:
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if features_extractor_class == FlattenExtractor:
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net_arch = [64, 64]
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else:
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net_arch = []
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self.net_arch = net_arch
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self.activation_fn = activation_fn
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self.normalize_images = normalize_images
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self.net_args = {
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"observation_space": self.observation_space,
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"action_space": self.action_space,
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"net_arch": self.net_arch,
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"activation_fn": self.activation_fn,
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"normalize_images": normalize_images,
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}
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self.q_net, self.q_net_target = None, None
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self._build(lr_schedule)
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def _build(self, lr_schedule: Schedule) -> None:
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"""
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Create the network and the optimizer.
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:param lr_schedule: Learning rate schedule
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lr_schedule(1) is the initial learning rate
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"""
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self.q_net = self.make_q_net()
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self.q_net_target = self.make_q_net()
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self.q_net_target.load_state_dict(self.q_net.state_dict())
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# Setup optimizer with initial learning rate
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self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
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def make_q_net(self) -> QNetwork:
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# Make sure we always have separate networks for features extractors etc
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net_args = self._update_features_extractor(self.net_args, features_extractor=None)
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return QNetwork(**net_args).to(self.device)
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def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
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return self._predict(obs, deterministic=deterministic)
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def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
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return self.q_net._predict(obs, deterministic=deterministic)
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def _get_constructor_parameters(self) -> Dict[str, Any]:
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data = super()._get_constructor_parameters()
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data.update(
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dict(
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net_arch=self.net_args["net_arch"],
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activation_fn=self.net_args["activation_fn"],
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lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
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optimizer_class=self.optimizer_class,
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optimizer_kwargs=self.optimizer_kwargs,
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features_extractor_class=self.features_extractor_class,
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features_extractor_kwargs=self.features_extractor_kwargs,
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)
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)
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return data
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MlpPolicy = DQNPolicy
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class CnnPolicy(DQNPolicy):
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"""
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Policy class for DQN when using images as input.
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:param observation_space: Observation space
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:param action_space: Action space
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:param lr_schedule: Learning rate schedule (could be constant)
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:param net_arch: The specification of the policy and value networks.
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:param activation_fn: Activation function
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:param features_extractor_class: Features extractor to use.
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:param normalize_images: Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: Additional keyword arguments,
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excluding the learning rate, to pass to the optimizer
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"""
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def __init__(
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self,
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observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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lr_schedule: Schedule,
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net_arch: Optional[List[int]] = None,
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activation_fn: Type[nn.Module] = nn.ReLU,
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features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
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features_extractor_kwargs: Optional[Dict[str, Any]] = None,
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normalize_images: bool = True,
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optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
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optimizer_kwargs: Optional[Dict[str, Any]] = None,
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):
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super(CnnPolicy, self).__init__(
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observation_space,
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action_space,
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lr_schedule,
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net_arch,
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activation_fn,
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features_extractor_class,
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features_extractor_kwargs,
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normalize_images,
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optimizer_class,
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optimizer_kwargs,
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)
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register_policy("MlpPolicy", MlpPolicy)
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register_policy("CnnPolicy", CnnPolicy)
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