class_docstr: Vectorized Worker that collects partial episodes.
  
  Useful for off-policy RL.
  
  Args:
      seed (int): The seed to use to intialize random number generators.
      max_episode_length (int or float): The maximum length of episodes which
          will be sampled. Can be (floating point) infinity.
      length of fragments before they're transmitted out of
      worker_number (int): The number of the worker this update is
          occurring in. This argument is used to set a different seed for
          each worker.
      n_envs (int): Number of environment copies to use.
      timesteps_per_call (int): Maximum number of timesteps to gather per env
          per call to the worker. Defaults to 1 (i.e. gather 1 timestep per
          env each call, or n_envs timesteps in total each call).
docstr: Resets all agents if the environment was updated.
docstr_tok: ["Resets","all","agents","if","the","environment","was","updated","."]
code: def start_episode(self):
      """Resets all agents if the environment was updated."""
      if self._needs_env_reset:
          self._needs_env_reset = False
          self.agent.reset([True] * len(self._envs))
          self._episode_lengths = [0] * len(self._envs)
          self._fragments = [
              InProgressEpisode(env) for env in self._envs
          ]
  
file_path: garage/src/garage/sampler/fragment_worker.py
imports: import copy
  import numpy
  from garage import EpisodeBatch, StepType
  from garage.sampler import _apply_env_update, InProgressEpisode
  from garage.sampler.default_worker import DefaultWorker
label_desc: Use image node_id 3 for calling the FragmentWorker obj's underlying member method code with example usage: obj.start_episode() without return types