class_docstr: DQN agent.
  
  This implements a single-process DQN agent. This is a simple Q-learning
  algorithm that inserts N-step transitions into a replay buffer, and
  periodically updates its policy by sampling these transitions using
  prioritization.
class_docstr_tok: ["DQN","agent",".","This","implements","a","single-process","DQN","agent",".","This","is","a","simple","Q-learning","algorithm","that","inserts","N-step","transitions","into","a","replay","buffer",",","and","periodically","updates","its","policy","by","sampling","these","transitions","using","prioritization","."]
code: def update(self):
      super().update()
      if self._checkpointer is not None:
          self._checkpointer.save()
  
file_path: acme/acme/agents/tf/dqn/agent.py
imports: import copy
  from typing import Optional
  from acme import datasets
  from acme import specs
  from acme.adders import reverb
  from acme.agents import agent
  from acme.agents.tf import actors
  from acme.agents.tf.dqn import learning
  from acme.tf import savers
  from acme.tf import utils
  from acme.utils import loggers
  import reverb
  import sonnet
  import tensorflow
  import trfl
label_desc: Use image node_id 2 for calling the DQN obj's underlying member method code with example usage: obj.update() without return types