def _run_one_iteration(self, iteration):
"""Runs one iteration of agent/environment interaction.
An iteration involves running several episodes until a certain number of
steps are obtained. The interleaving of train/eval phases implemented here
are to match the implementation of (Mnih et al., 2015).
Args:
iteration: int, current iteration number, used as a global_step for saving
Tensorboard summaries.
Returns:
A dict containing summary statistics for this iteration.
"""
statistics = iteration_statistics.IterationStatistics()
tf.logging.info('Starting iteration %d', iteration)
num_episodes_train, average_reward_train = self._run_train_phase(
statistics)
num_episodes_eval, average_reward_eval = self._run_eval_phase(
statistics)
self._save_tensorboard_summaries(iteration, num_episodes_train,
average_reward_train,
num_episodes_eval,
average_reward_eval)
return statistics.data_lists
def testAddManyValues(self):
my_pi = 3.14159
statistics = iteration_statistics.IterationStatistics()
# Add a number of items. Each item is added to the list corresponding to its
# given key.
statistics.append({
'rewards': 0,
'nouns': 'reinforcement',
'angles': my_pi
})
# Add a second item to the 'nouns' list.
statistics.append({'nouns': 'learning'})
# There are three lists.
self.assertEqual(3, len(statistics.data_lists))
self.assertEqual(1, len(statistics.data_lists['rewards']))
self.assertEqual(2, len(statistics.data_lists['nouns']))
self.assertEqual(1, len(statistics.data_lists['angles']))
self.assertEqual(0, statistics.data_lists['rewards'][0])
self.assertEqual('reinforcement', statistics.data_lists['nouns'][0])
self.assertEqual('learning', statistics.data_lists['nouns'][1])
self.assertEqual(my_pi, statistics.data_lists['angles'][0])
def testAddOneValue(self):
statistics = iteration_statistics.IterationStatistics()
# The statistics data structure should be empty a-priori.
self.assertEqual(0, len(statistics.data_lists))
statistics.append({'key1': 0})
# We should have exactly one list, containing one value.
self.assertEqual(1, len(statistics.data_lists))
self.assertEqual(1, len(statistics.data_lists['key1']))
self.assertEqual(0, statistics.data_lists['key1'][0])
def _run_one_iteration(self, iteration):
"""Runs one iteration of agent/environment interaction.
An iteration involves running several episodes until a certain number of
steps are obtained. The interleaving of train/eval phases implemented here
are to match the implementation of (Mnih et al., 2015).
Args:
iteration: int, current iteration number, used as a global_step for saving
Tensorboard summaries.
Returns:
A dict containing summary statistics for this iteration.
"""
statistics = iteration_statistics.IterationStatistics()
tf.logging.info('Starting iteration %d', iteration)
train_eval_mode = False
# if self.game_name == "Pong":
if self.average_reward_eval >= episodic_return_switch[self.game_name]:
train_eval_mode = True
print("Stop training at iteration {}".format(iteration))
num_episodes_train, average_reward_train = self._run_train_phase(
statistics, train_eval_mode)
# else:
# # don't train, only for evaluation.
# num_episodes_train, average_reward_train = 0, 0
if self.agent_name in RPG_AGENTS and self._agent._replay_opt.memory.add_count == 0:
num_episodes_eval, average_reward_eval = -10000, -10000
# if we didn't train rpg, don't waste time evaluate it.
else:
num_episodes_eval, average_reward_eval = self._run_eval_phase(
statistics)
self.average_reward_eval = average_reward_eval
self._save_tensorboard_summaries(iteration, num_episodes_train,
average_reward_train,
num_episodes_eval,
average_reward_eval)
return statistics.data_lists
def _run_one_iteration(self, iteration):
"""Runs one iteration of agent/environment interaction.
An iteration involves running several episodes until a certain number of
steps are obtained. This method differs from the `_run_one_iteration` method
in the base `Runner` class in that it only runs the train phase.
Args:
iteration: int, current iteration number, used as a global_step for saving
Tensorboard summaries.
Returns:
A dict containing summary statistics for this iteration.
"""
statistics = iteration_statistics.IterationStatistics()
num_episodes_train, average_reward_train = self._run_train_phase(
statistics)
self._save_tensorboard_summaries(iteration, num_episodes_train,
average_reward_train)
return statistics.data_lists
def _run_one_iteration(self, iteration):
statistics = iteration_statistics.IterationStatistics()
print('Starting iteration ', iteration)
self._run_train_phase()
self._run_eval_phase(statistics)
return statistics.data_lists
def testMissingValue(self):
statistics = iteration_statistics.IterationStatistics()
with self.assertRaises(KeyError):
_ = statistics.data_lists['missing_key']
