class_docstr: Initialize a worker for RL2.
  
  In RL2, policy does not reset between epsiodes in each meta batch.
  Policy only resets once at the beginning of a trial/meta batch.
  
  Args:
      seed (int): The seed to use to intialize random number generators.
      max_episode_length (int or float): The maximum length of episodes to
          sample. Can be (floating point) infinity.
      worker_number (int): The number of the worker where this update is
          occurring. This argument is used to set a different seed for each
          worker.
      n_episodes_per_trial (int): Number of episodes sampled per
          trial/meta-batch. Policy resets in the beginning of a meta batch,
          and obtain `n_episodes_per_trial` episodes in one meta batch.
  
  Attributes:
      agent(Policy or None): The worker's agent.
      env(Environment or None): The worker's environment.
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docstr: Begin a new episode.
docstr_tok: ["Begin","a","new","episode","."]
code: def start_episode(self):
      """Begin a new episode."""
      self._eps_length = 0
      self._prev_obs = self.env.reset()[0]
  
imports: import abc
  import collections
  import akro
  from dowel import logger
  import numpy
  from garage import EnvSpec, EnvStep, EpisodeBatch, log_multitask_performance, StepType, Wrapper
  from garage.np.algos import MetaRLAlgorithm
  from garage.sampler import DefaultWorker
  from garage.tf.algos._rl2npo import RL2NPO
label_desc: Use image node_id 2 for calling the RL2Worker obj's underlying member method code with example usage: obj.start_episode() without return types