class_docstr: Tests for configuring Atari agents using gin.
  
    
class_docstr_tok: ["Tests","for","configuring","Atari","agents","using","gin","."]
docstr: Test DQNAgent configuration overridden with RMSPropOptimizer.
docstr_tok: ["Test","DQNAgent","configuration","overridden","with","RMSPropOptimizer","."]
code: def testDefaultGinRmspropDqn(self):
      """Test DQNAgent configuration overridden with RMSPropOptimizer."""
      logging.info("####### Training the DQN agent #####")
      logging.info("####### DQN base_dir: %s", self._base_dir)
      gin_files = ["dopamine/agents/dqn/configs/dqn.gin"]
      gin_bindings = [
          "DQNAgent.optimizer = @tf.train.RMSPropOptimizer()",
          "tf.train.RMSPropOptimizer.learning_rate = 100",
          "WrappedReplayBuffer.replay_capacity = 100",  # To prevent OOM.
          "create_agent.agent_name = 'dqn'",
      ]
      run_experiment.load_gin_configs(gin_files, gin_bindings)
      runner = run_experiment.Runner(
          self._base_dir,
          run_experiment.create_agent,
          atari_lib.create_atari_environment,
      )
      self.assertIsInstance(
          runner._agent.optimizer, tf.compat.v1.train.RMSPropOptimizer
      )
      self.assertEqual(100, runner._agent.optimizer._learning_rate)
      shutil.rmtree(self._base_dir)
  
imports: import datetime
  import os
  import shutil
  from absl import flags
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from dopamine.discrete_domains import run_experiment
  import gin.tf
  import tensorflow
label_desc: Use image node_id 4 for calling the GinConfigTest obj's underlying member method code with example usage: obj.testDefaultGinRmspropDqn() without return types