class_docstr: :param pal_alpha: (float)
      A factor that weights the amount by which the advantage learning update will be taken into account.
  
  :param persistent_advantage_learning: (bool)
      If set to True, the persistent mode of advantage learning will be used, which encourages the agent to take
      the same actions one after the other instead of changing actions.
  
  :param monte_carlo_mixing_rate: (float)
      The amount of monte carlo values to mix into the targets of the network. The monte carlo values are just the
      total discounted returns, and they can help reduce the time it takes for the network to update to the newly
      seen values, since it is not based on bootstrapping the current network values.
class_docstr_tok: [":","param","pal_alpha",":","(","float",")","A","factor","that","weights","the","amount","by","which","the","advantage","learning","update","will","be","taken","into","account",".",":","param","persistent_advantage_learning",":","(","bool",")","If","set","to","True",",","the","persistent","mode","of","advantage","learning","will","be","used",",","which","encourages","the","agent","to","take","the","same","actions","one","after","the","other","instead","of","changing","actions",".",":","param","monte_carlo_mixing_rate",":","(","float",")","The","amount","of","monte","carlo","values","to","mix","into","the","targets","of","the","network",".","The","monte","carlo","values","are","just","the","total","discounted","returns",",","and","they","can","help","reduce","the","time","it","takes","for","the","network","to","update","to","the","newly","seen","values",",","since","it","is","not","based","on","bootstrapping","the","current","network","values","."]
code: def __init__(self):
      super().__init__()
      self.pal_alpha = 0.9
      self.persistent_advantage_learning = False
      self.monte_carlo_mixing_rate = 0.1
  
file_path: coach/rl_coach/agents/pal_agent.py
imports: from typing import Union
  import numpy
  from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
label_desc: Use image node_id 1 to create a new PALAlgorithmParameters object from inherited base classes: DQNAlgorithmParameters with example: obj = PALAlgorithmParameters()