class_docstr: A Qtopt Agent.
  
  Implements the Qt-Opt algorithm from
  
  "QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic "
    "Manipulation"
  
   Dmitry Kalashnikov et al., 2018
   https://arxiv.org/abs/1806.10293
code: def _initialize(self):
      common.soft_variables_update(
          self._q_network.variables,
          self._target_q_network.variables,
          tau=1.0,
      )
      if self._enable_td3:
          assert (
              self._target_q_network_delayed is not None
          )  # When _enable_td3.
          common.soft_variables_update(
              self._q_network.variables,
              self._target_q_network_delayed.variables,
              tau=1.0,
          )
          common.soft_variables_update(
              self._q_network.variables,
              self._target_q_network_delayed_2.variables,
              tau=1.0,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import typing
  import gin
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.networks import utils
  from tf_agents.policies import epsilon_greedy_policy
  from tf_agents.policies import qtopt_cem_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 7 for calling the QtOptAgent obj's underlying member method code with example usage: obj._initialize() without return types