class_docstr: :param atoms: (int)
      the number of atoms to predict for each action
  
  :param huber_loss_interval: (float)
      One of the huber loss parameters, and is referred to as :math:`\kapa` in the paper.
      It describes the interval [-k, k] in which the huber loss acts as a MSE loss.
class_docstr_tok: [":","param","atoms",":","(","int",")","the","number","of","atoms","to","predict","for","each","action",":","param","huber_loss_interval",":","(","float",")","One","of","the","huber","loss","parameters",",","and","is","referred","to","as",":","math",":","`","\\kapa","`","in","the","paper",".","It","describes","the","interval","[","-k",",","k","]","in","which","the","huber","loss","acts","as","a","MSE","loss","."]
code: def __init__(self):
      super().__init__()
      self.atoms = 200
      self.huber_loss_interval = 1
  
imports: from copy import copy
  from typing import Union
  import numpy
  from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.head_parameters import QuantileRegressionQHeadParameters
  from rl_coach.core_types import StateType
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 1 to create a new QuantileRegressionDQNAlgorithmParameters object from inherited base classes: DQNAlgorithmParameters with example: obj = QuantileRegressionDQNAlgorithmParameters()