class_docstr: Deep Deterministic Policy Gradients.
  
  This can be used as SVG(0) by specifying a Gaussian policy instead of a
  deterministic policy.
  
  Args:
      model (DDPGModel): DDPG model that contains both a policy and a
          Q-function
      actor_optimizer (Optimizer): Optimizer setup with the policy
      critic_optimizer (Optimizer): Optimizer setup with the Q-function
      replay_buffer (ReplayBuffer): Replay buffer
      gamma (float): Discount factor
      explorer (Explorer): Explorer that specifies an exploration strategy.
      gpu (int): GPU device id if not None nor negative.
      replay_start_size (int): if the replay buffer's size is less than
          replay_start_size, skip update
      minibatch_size (int): Minibatch size
      update_interval (int): Model update interval in step
      target_update_interval (int): Target model update interval in step
      phi (callable): Feature extractor applied to observations
      target_update_method (str): 'hard' or 'soft'.
      soft_update_tau (float): Tau of soft target update.
      n_times_update (int): Number of repetition of update
      average_q_decay (float): Decay rate of average Q, only used for
          recording statistics
      average_loss_decay (float): Decay rate of average loss, only used for
          recording statistics
      batch_accumulator (str): 'mean' or 'sum'
      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
      logger (Logger): Logger used
      batch_states (callable): method which makes a batch of observations.
          default is `chainerrl.misc.batch_states.batch_states`
      burnin_action_func (callable or None): If not None, this callable
          object is used to select actions before the model is updated
          one or more times during training.
docstr: Observe a batch of action consequences for training.
  
  Args:
      batch_obs (Sequence of ~object): Observations.
      batch_reward (Sequence of float): Rewards.
      batch_done (Sequence of boolean): Boolean values where True
          indicates the current state is terminal.
      batch_reset (Sequence of boolean): Boolean values where True
          indicates the current episode will be reset, even if the
          current state is not terminal.
  
  Returns:
      None
docstr_tok: ["Observe","a","batch","of","action","consequences","for","training",".","Args",":","batch_obs","(","Sequence","of","~object",")",":","Observations",".","batch_reward","(","Sequence","of","float",")",":","Rewards",".","batch_done","(","Sequence","of","boolean",")",":","Boolean","values","where","True","indicates","the","current","state","is","terminal",".","batch_reset","(","Sequence","of","boolean",")",":","Boolean","values","where","True","indicates","the","current","episode","will","be","reset",",","even","if","the","current","state","is","not","terminal",".","Returns",":","None"]
code: def batch_observe_and_train(
      self, batch_obs, batch_reward, batch_done, batch_reset
  ):
      """Observe a batch of action consequences for training.
  
      Args:
          batch_obs (Sequence of ~object): Observations.
          batch_reward (Sequence of float): Rewards.
          batch_done (Sequence of boolean): Boolean values where True
              indicates the current state is terminal.
          batch_reset (Sequence of boolean): Boolean values where True
              indicates the current episode will be reset, even if the
              current state is not terminal.
  
      Returns:
          None
      """
      for i in range(len(batch_obs)):
          self.t += 1
          # Update the target network
          if self.t % self.target_update_interval == 0:
              self.sync_target_network()
          if self.batch_last_obs[i] is not None:
              assert self.batch_last_action[i] is not None
              # Add a transition to the replay buffer
              self.replay_buffer.append(
                  state=self.batch_last_obs[i],
                  action=self.batch_last_action[i],
                  reward=batch_reward[i],
                  next_state=batch_obs[i],
                  next_action=None,
                  is_state_terminal=batch_done[i],
                  env_id=i,
              )
              if batch_reset[i] or batch_done[i]:
                  self.batch_last_obs[i] = None
                  self.replay_buffer.stop_current_episode(env_id=i)
          self.replay_updater.update_if_necessary(self.t)
  
imports: import copy
  from logging import getLogger
  import chainer
  from chainer import cuda
  import chainer.functions
  from chainerrl.agent import AttributeSavingMixin
  from chainerrl.agent import BatchAgent
  from chainerrl.misc.batch_states import batch_states
  from chainerrl.misc.copy_param import synchronize_parameters
  from chainerrl.recurrent import Recurrent
  from chainerrl.recurrent import RecurrentChainMixin
  from chainerrl.recurrent import state_kept
  from chainerrl.replay_buffer import batch_experiences
  from chainerrl.replay_buffer import ReplayUpdater
label_desc: Use image node_id 11 for calling the DDPG obj's underlying member method code with example usage: obj.batch_observe_and_train(batch_obs, batch_reward, batch_done, batch_reset) without return types