class_docstr: Deep Q-Network algorithm.
  
  Args:
      q_function (StateQFunction): Q-function
      optimizer (Optimizer): Optimizer that is already setup
      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
      clip_delta (bool): Clip delta if set True
      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_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`
      recurrent (bool): If set to True, `model` is assumed to implement
          `chainerrl.links.StatelessRecurrent` and is updated in a recurrent
          manner.
class_docstr_tok: ["Deep","Q-Network","algorithm",".","Args",":","q_function","(","StateQFunction",")",":","Q-function","optimizer","(","Optimizer",")",":","Optimizer","that","is","already","setup","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","clip_delta","(","bool",")",":","Clip","delta","if","set","True","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_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","`","recurrent","(","bool",")",":","If","set","to","True",",","`","model","`","is","assumed","to","implement","`","chainerrl.links.StatelessRecurrent","`","and","is","updated","in","a","recurrent","manner","."]
code: def act_and_train(self, obs, reward):
      # Observe the consequences
      if self.last_state is not None:
          assert self.last_action is not None
          # Add a transition to the replay buffer
          transition = {
              "state": self.last_state,
              "action": self.last_action,
              "reward": reward,
              "next_state": obs,
              "is_state_terminal": False,
          }
          if self.recurrent:
              transition[
                  "recurrent_state"
              ] = self.model.get_recurrent_state_at(
                  self.train_prev_recurrent_states,
                  0,
                  unwrap_variable=True,
              )
              self.train_prev_recurrent_states = None
              transition[
                  "next_recurrent_state"
              ] = self.model.get_recurrent_state_at(
                  self.train_recurrent_states, 0, unwrap_variable=True
              )
          self.replay_buffer.append(**transition)
  
      # Update the target network
      if self.t % self.target_update_interval == 0:
          self.sync_target_network()
  
      # Update the model
      self.replay_updater.update_if_necessary(self.t)
  
      # Choose an action
      with chainer.using_config(
          "train", False
      ), chainer.no_backprop_mode():
          action_value = (
              self._evaluate_model_and_update_recurrent_states(
                  [obs], test=False
              )
          )
          q = float(action_value.max.array)
          greedy_action = cuda.to_cpu(
              action_value.greedy_actions.array
          )[0]
      action = self.explorer.select_action(
          self.t, lambda: greedy_action, action_value=action_value
      )
  
      # Update stats
      self.average_q *= self.average_q_decay
      self.average_q += (1 - self.average_q_decay) * q
  
      self.t += 1
      self.last_state = obs
      self.last_action = action
  
      self.logger.debug(
          "t:%s q:%s action_value:%s", self.t, q, action_value
      )
      self.logger.debug("t:%s r:%s a:%s", self.t, reward, action)
  
      return self.last_action
  
imports: import copy
  from logging import getLogger
  import chainer
  from chainer import cuda
  import chainer.functions
  from chainerrl import agent
  from chainerrl.misc.batch_states import batch_states
  from chainerrl.misc.copy_param import synchronize_parameters
  from chainerrl.replay_buffer import batch_experiences
  from chainerrl.replay_buffer import batch_recurrent_experiences
  from chainerrl.replay_buffer import ReplayUpdater
label_desc: Use image node_id 9 for calling the DQN obj's underlying member method code with example usage: obj.act_and_train(obs, reward) and returns: self