class_docstr: :param num_steps_between_copying_online_weights_to_target: (StepMethod)
      The number of steps between copying the online network weights to the target network weights.
  
  :param rate_for_copying_weights_to_target: (float)
      When copying the online network weights to the target network weights, a soft update will be used, which
      weight the new online network weights by rate_for_copying_weights_to_target. (Tau as defined in the paper)
  
  :param use_deterministic_for_evaluation: (bool)
      If True, during the evaluation phase, action are chosen deterministically according to the policy mean
      and not sampled from the policy distribution.
code: def __init__(self):
      super().__init__()
      self.num_steps_between_copying_online_weights_to_target = (
          EnvironmentSteps(1)
      )
      self.rate_for_copying_weights_to_target = 0.005
      self.use_deterministic_for_evaluation = True
  
imports: from typing import Union
  import copy
  import numpy
  from collections import OrderedDict
  from rl_coach.agents.agent import Agent
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.architectures.head_parameters import SACQHeadParameters, SACPolicyHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, EmbedderScheme, MiddlewareScheme
  from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.spaces import BoxActionSpace
label_desc: Use image node_id 1 to create a new SoftActorCriticAlgorithmParameters object from inherited base classes: AlgorithmParameters with example: obj = SoftActorCriticAlgorithmParameters()