code: def __init__(self):
      super().__init__(
          algorithm=DQNAlgorithmParameters(),
          exploration=EGreedyParameters(),
          memory=ExperienceReplayParameters(),
          networks={"main": DQNNetworkParameters()},
      )
      self.exploration.epsilon_schedule = LinearSchedule(
          1, 0.1, 1000000
      )
      self.exploration.evaluation_epsilon = 0.05
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, MiddlewareScheme
  from rl_coach.core_types import EnvironmentSteps
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 1 to create a new DQNAgentParameters object from inherited base classes: AgentParameters with example: obj = DQNAgentParameters()