class_docstr: Object that handles update schedule and configurations.
  
  Args:
      replay_buffer (ReplayBuffer): Replay buffer
      update_func (callable): Callable that accepts one of these:
          (1) a list of transition dicts (if episodic_update=False)
          (2) a list of lists of transition dicts (if episodic_update=True)
      replay_start_size (int): if the replay buffer's size is less than
          replay_start_size, skip update
      batchsize (int): Minibatch size
      update_interval (int): Model update interval in step
      n_times_update (int): Number of repetition of update
      episodic_update (bool): Use full episodes for update if set True
      episodic_update_len (int or None): Subsequences of this length are used
          for update if set int and episodic_update=True
class_docstr_tok: ["Object","that","handles","update","schedule","and","configurations",".","Args",":","replay_buffer","(","ReplayBuffer",")",":","Replay","buffer","update_func","(","callable",")",":","Callable","that","accepts","one","of","these",":","(","1",")","a","list","of","transition","dicts","(","if","episodic_update=False",")","(","2",")","a","list","of","lists","of","transition","dicts","(","if","episodic_update=True",")","replay_start_size","(","int",")",":","if","the","replay","buffer","'s","size","is","less","than","replay_start_size",",","skip","update","batchsize","(","int",")",":","Minibatch","size","update_interval","(","int",")",":","Model","update","interval","in","step","n_times_update","(","int",")",":","Number","of","repetition","of","update","episodic_update","(","bool",")",":","Use","full","episodes","for","update","if","set","True","episodic_update_len","(","int","or","None",")",":","Subsequences","of","this","length","are","used","for","update","if","set","int","and","episodic_update=True"]
code: def __init__(
      self,
      replay_buffer,
      update_func,
      batchsize,
      episodic_update,
      n_times_update,
      replay_start_size,
      update_interval,
      episodic_update_len=None,
  ):
      assert batchsize <= replay_start_size
      self.replay_buffer = replay_buffer
      self.update_func = update_func
      self.batchsize = batchsize
      self.episodic_update = episodic_update
      self.episodic_update_len = episodic_update_len
      self.n_times_update = n_times_update
      self.replay_start_size = replay_start_size
      self.update_interval = update_interval
  
imports: from abc import ABCMeta, abstractmethod, abstractproperty
  from typing import Optional
  import numpy
  import torch
  from pfrl.utils.batch_states import batch_states
  from pfrl.utils.recurrent import concatenate_recurrent_states, flatten_sequences_time_first, recurrent_state_from_numpy
label_desc: Use image node_id 1 to create a new ReplayUpdater object from inherited base classes: object with example: obj = ReplayUpdater(replay_buffer, update_func, batchsize, episodic_update, n_times_update, replay_start_size, update_interval, episodic_update_len)