class_docstr: :param action_drop_method_parameters: (Parameters)
      Defines the mode and related parameters according to which low confidence actions will be filtered out
  :param num_steps_between_copying_online_weights_to_target (StepMethod)
      Defines the number of steps between every phase of copying online network's weights to the target network's weights
class_docstr_tok: [":","param","action_drop_method_parameters",":","(","Parameters",")","Defines","the","mode","and","related","parameters","according","to","which","low","confidence","actions","will","be","filtered","out",":","param","num_steps_between_copying_online_weights_to_target","(","StepMethod",")","Defines","the","number","of","steps","between","every","phase","of","copying","online","network","'s","weights","to","the","target","network","'s","weights"]
code: def __init__(self):
      super().__init__()
      self.action_drop_method_parameters = KNNParameters()
      self.num_steps_between_copying_online_weights_to_target = (
          EnvironmentSteps(30000)
      )
  
imports: from collections import OrderedDict
  from copy import deepcopy
  from typing import Union, List, Dict
  import numpy
  from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters, DQNAgent
  from rl_coach.base_parameters import Parameters
  from rl_coach.core_types import EnvironmentSteps, Batch, StateType
  from rl_coach.graph_managers.batch_rl_graph_manager import BatchRLGraphManager
  from rl_coach.logger import screen
  from rl_coach.memories.non_episodic.differentiable_neural_dictionary import AnnoyDictionary
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 1 to create a new DDQNBCQAlgorithmParameters object from inherited base classes: DQNAlgorithmParameters with example: obj = DDQNBCQAlgorithmParameters()