docstr: Initialize any modules that depend on knowing information about the environment such as the action space or
  the observation space
  
  :return: None
docstr_tok: ["Initialize","any","modules","that","depend","on","knowing","information","about","the","environment","such","as","the","action","space","or","the","observation","space",":","return",":","None"]
code: def init_environment_dependent_modules(self) -> None:
      """
      Initialize any modules that depend on knowing information about the environment such as the action space or
      the observation space
  
      :return: None
      """
      # initialize exploration policy
      if isinstance(self.ap.exploration, dict):
          if self.spaces.action.__class__ in self.ap.exploration.keys():
              self.ap.exploration = self.ap.exploration[
                  self.spaces.action.__class__
              ]
          else:
              raise ValueError(
                  "The exploration parameters were defined as a mapping between action space types and "
                  "exploration types, but the action space used by the environment ({}) was not part of "
                  "the exploration parameters dictionary keys ({})".format(
                      self.spaces.action.__class__,
                      list(self.ap.exploration.keys()),
                  )
              )
      self.ap.exploration.action_space = self.spaces.action
      self.exploration_policy = (
          dynamic_import_and_instantiate_module_from_params(
              self.ap.exploration
          )
      )
  
      # create all the networks of the agent
      self.networks = self.create_networks()
  
imports: import copy
  import random
  from collections import OrderedDict
  from typing import Dict, List, Union, Tuple
  import numpy
  from six.moves import range
  from rl_coach.agents.agent_interface import AgentInterface
  from rl_coach.architectures.network_wrapper import NetworkWrapper
  from rl_coach.base_parameters import AgentParameters, Device, DeviceType, DistributedTaskParameters, Frameworks
  from rl_coach.core_types import RunPhase, PredictionType, EnvironmentEpisodes, ActionType, Batch, Episode, StateType
  from rl_coach.core_types import Transition, ActionInfo, TrainingSteps, EnvironmentSteps, EnvResponse
  from rl_coach.logger import screen, Logger, EpisodeLogger
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplay
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition, VectorObservationSpace, GoalsSpace, AttentionActionSpace
  from rl_coach.utils import Signal, force_list
  from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
  from rl_coach.memories.backend.memory_impl import get_memory_backend
  from rl_coach.core_types import TimeTypes
  from rl_coach.off_policy_evaluators.ope_manager import OpeManager
  from rl_coach.core_types import PickledReplayBuffer, CsvDataset
label_desc: Use image node_id 11 for calling the Agent obj's underlying member method code with example usage: obj.init_environment_dependent_modules() without return types