class_docstr: :param monte_carlo_mixing_rate: (float)
      The mixing rate is used for setting the amount of monte carlo estimate (full return) that will be mixes into
      the single-step bootstrapped targets.
class_docstr_tok: [":","param","monte_carlo_mixing_rate",":","(","float",")","The","mixing","rate","is","used","for","setting","the","amount","of","monte","carlo","estimate","(","full","return",")","that","will","be","mixes","into","the","single-step","bootstrapped","targets","."]
code: def __init__(self):
      super().__init__()
      self.monte_carlo_mixing_rate = 0.1
  
file_path: coach/rl_coach/agents/mmc_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 MixedMonteCarloAlgorithmParameters object from inherited base classes: DQNAlgorithmParameters with example: obj = MixedMonteCarloAlgorithmParameters()