class_docstr: Runner class to run Tandem RL experiments.
class_docstr_tok: ["Runner","class","to","run","Tandem","RL","experiments","."]
docstr: Initialization for a new episode.
  
  Args:
    agent_type: str, the type of agent to run.
  
  Returns:
    action: int, the initial action chosen by the agent.
docstr_tok: ["Initialization","for","a","new","episode",".","Args",":","agent_type",":","str",",","the","type","of","agent","to","run",".","Returns",":","action",":","int",",","the","initial","action","chosen","by","the","agent","."]
code: def _initialize_episode(self, agent_type="active"):
      """Initialization for a new episode.
  
      Args:
        agent_type: str, the type of agent to run.
  
      Returns:
        action: int, the initial action chosen by the agent.
      """
      initial_observation = self._environment.reset()
      return self._agent.begin_episode(agent_type, initial_observation)
  
imports: import sys
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from dopamine.discrete_domains import gym_lib
  from dopamine.discrete_domains import iteration_statistics
  from dopamine.discrete_domains import run_experiment
  from dopamine.labs.environments.minatar import minatar_env
  from dopamine.labs.tandem_dqn import tandem_dqn_agent
  import gin
  import numpy
  import tensorflow
label_desc: Use image node_id 2 for calling the TandemRunner obj's underlying member method code with example usage: obj._initialize_episode(agent_type) and returns: self