def testOverrideRunnerParams(self):
"""Test DQNAgent configuration using the default gin config."""
tf.logging.info('####### Training the DQN agent #####')
tf.logging.info('####### DQN base_dir: {}'.format(FLAGS.base_dir))
FLAGS.agent_name = 'dqn'
FLAGS.gin_files = ['dopamine/agents/dqn/configs/dqn.gin']
FLAGS.gin_bindings = [
'TrainRunner.base_dir = "{}"'.format(FLAGS.base_dir),
'Runner.log_every_n = 1729',
'WrappedReplayBuffer.replay_capacity = 100' # To prevent OOM.
]
run_experiment.load_gin_configs(FLAGS.gin_files, FLAGS.gin_bindings)
runner = run_experiment.TrainRunner(create_agent_fn=train.create_agent)
self.assertEqual(runner._base_dir, FLAGS.base_dir)
self.assertEqual(runner._log_every_n, 1729)
shutil.rmtree(FLAGS.base_dir)
def create_runner(base_dir, create_agent_fn):
"""Creates an experiment Runner.
Args:
base_dir: str, base directory for hosting all subdirectories.
create_agent_fn: A function that takes as args a Tensorflow session and an
Atari 2600 Gym environment, and returns an agent.
Returns:
runner: A `run_experiment.Runner` like object.
Raises:
ValueError: When an unknown schedule is encountered.
"""
assert base_dir is not None
# Continuously runs training and evaluation until max num_iterations is hit.
if FLAGS.schedule == 'continuous_train_and_eval':
return run_experiment.Runner(base_dir, create_agent_fn, create_pacman_environment)
# Continuously runs training until max num_iterations is hit.
elif FLAGS.schedule == 'continuous_train':
return run_experiment.TrainRunner(base_dir, create_agent_fn, create_pacman_environment)
else:
raise ValueError('Unknown schedule: {}'.format(FLAGS.schedule))