class_docstr: Actor-Critic Policy Gradient.
  
  See https://arxiv.org/pdf/1602.01783.pdf for a description of algorithm.
docstr: Start a new episode.
  
  Args:
      state (object): observation provided by the environment.
  
  Returns:
      action (int): action choosen by agent.
      debug_info (dict): auxiliary diagnostic information.
docstr_tok: ["Start","a","new","episode",".","Args",":","state","(","object",")",":","observation","provided","by","the","environment",".","Returns",":","action","(","int",")",":","action","choosen","by","agent",".","debug_info","(","dict",")",":","auxiliary","diagnostic","information","."]
code: def start(self, state):
      """
      Start a new episode.
  
      Args:
          state (object): observation provided by the environment.
  
      Returns:
          action (int): action choosen by agent.
          debug_info (dict): auxiliary diagnostic information.
      """
      # Call _process_accumulated_trajectory() to process unused trajectory
      # data from previous episode.
      self._process_accumulated_trajectory(False)
  
      # Reset preprocessor.
      if self._preprocessor is not None:
          self._preprocessor.reset()
  
      # Append new state and action
      o = self._preprocess_state(state)
      action, _ = self._choose_action(o)
      self._trajectory_states.append(o)
      self._trajectory_actions.append(action)
  
      self.episode_count += 1
  
      return action, {}
  
imports: import cntk
  import numpy
  import ast
  from .agent import AgentBaseClass
  from .shared.cntk_utils import negative_of_entropy_with_softmax
  from .shared.models import Models
  from .shared.policy_gradient_parameters import PolicyGradientParameters
label_desc: Use image node_id 2 for calling the ActorCritic obj's underlying member method code with example usage: obj.start(state) and returns: action,  dict