def training_loop(self, is_testing, reward_from_agent=True):
start_time = time.time()
#current_episodes = 0
if is_testing:
rp.report("\n\n> Playing")
max_episodes = self.max_test_episodes
max_steps = self.max_steps_testing
current_episodes = self.curr_playing_episodes
else:
rp.report("> Training")
max_episodes = self.max_training_episodes
max_steps = self.max_steps_training
current_episodes = self.curr_training_episodes
if self.logger.ep_count == 0 or is_testing:
self.logger = Logger(
max_episodes,
self.agent.__class__.__name__,
self.agent.model.__class__.__name__,
self.agent.model,
self.agent.action_wrapper.__class__.__name__,
self.agent.action_wrapper.get_action_space_dim(),
self.agent.action_wrapper.get_named_actions(),
self.agent.state_builder.__class__.__name__,
self.agent.reward_builder.__class__.__name__,
self.env.__class__.__name__,
log_actions=self.log_actions,
episode_batch_avg_calculation=self.
episode_batch_avg_calculation,
rolling_avg_window_size=self.rolling_avg_window_size)
while current_episodes < max_episodes:
current_episodes += 1
self.env.start()
if is_testing:
self.curr_playing_episodes = current_episodes
else:
self.curr_training_episodes = current_episodes
# Reset the environment
obs = self.env.reset()
step_reward = 0
done = False
# Passing the episode to the agent reset, so that it can be passed to model reset
# Allowing the model to track the episode number, and decide if it should diminish the
# Learning Rate, depending on the currently selected strategy.
self.agent.reset(current_episodes)
ep_reward = 0
victory = False
ep_actions = np.zeros(
self.agent.action_wrapper.get_action_space_dim())
self.logger.record_episode_start()
for step in range(max_steps):
# Choosing an action and passing it to our env.step() in order to act on our environment
action = self.agent.step(obs, done, is_testing)
# Take the action (a) and observe the outcome state (s') and reward (r)
obs, default_reward, done = self.env.step(action)
# Logic to test wheter this is the last step of this episode
is_last_step = step == max_steps - 1
done = done or is_last_step
# Checking whether or not to use the reward from the reward builder so we can pass that to the agent
if reward_from_agent:
step_reward = self.agent.get_reward(
obs, default_reward, done)
else:
step_reward = default_reward
# Making the agent learn
if not is_testing:
self.agent.learn(obs, step_reward, done)
# Adding our step reward to the total count of the episode's reward
ep_reward += step_reward
ep_actions[self.agent.previous_action] += 1
if done:
victory = default_reward == 1
agent_info = {
"Learning rate": self.agent.model.learning_rate,
"Gamma": self.agent.model.gamma,
"Epsilon": self.agent.model.epsilon_greedy,
}
self.logger.record_episode(ep_reward, victory, step + 1,
agent_info, ep_actions)
break
self.logger.log_ep_stats()
# check if user wants to pause training and test agent
# if self.do_reward_test and current_episodes % self.episode_batch_avg_calculation == 0 and current_episodes > 1:
if (
not is_testing
) and self.do_reward_test and current_episodes % self.episode_batch_avg_calculation == 0:
self.test_agent()
# if this is not a test (evaluation), saving is enabled and we are in a multiple
# of our save_every variable then we save the model and generate graphs
if (
not is_testing
) and self.enable_save and current_episodes > 0 and current_episodes % self.save_every == 0:
self.save(self.full_save_path)
# if we have done tests along the training save all loggers for further detailed analysis
if self.do_reward_test and len(
self.inside_training_test_loggers) > 0:
for idx in range(
len(self.logger.ep_avg_batch_rewards_episodes)):
logger_dict = self.inside_training_test_loggers[idx]
if not logger_dict["saved"]:
episode = self.logger.ep_avg_batch_rewards_episodes[
idx]
backup_full_save_path = self.full_save_path
self.full_save_path = self.full_save_path + os.path.sep + "inside_training_play_files" + os.path.sep + "test_at_training_episode_{}".format(
episode)
self.make_persistance_dirs(self.log_actions)
logger_dict["logger"].save(self.full_save_path)
logger_dict["saved"] = True
self.full_save_path = backup_full_save_path
end_time = time.time()
if is_testing:
rp.report("\n> Test duration: {} seconds".format(end_time -
start_time))
self.logger.log_train_stats()
else:
rp.report("\n> Training duration: {} seconds".format(end_time -
start_time))
self.logger.log_train_stats()
# Saving the model at the end of the training loop
if self.enable_save:
if is_testing:
self.logger.save(self.full_save_play_path)
rp.save(self.full_save_play_path)
else:
self.save(self.full_save_path)
# if we have done tests along the training save all loggers for further detailed analysis
if self.do_reward_test and len(
self.inside_training_test_loggers) > 0:
for idx in range(
len(self.logger.ep_avg_batch_rewards_episodes)):
logger_dict = self.inside_training_test_loggers[idx]
if not logger_dict["saved"]:
episode = self.logger.ep_avg_batch_rewards_episodes[
idx]
backup_full_save_path = self.full_save_path
self.full_save_path = self.full_save_path + os.path.sep + "inside_training_play_files" + os.path.sep + "test_at_training_episode_{}".format(
episode)
self.make_persistance_dirs(self.log_actions)
logger_dict["logger"].save(self.full_save_path)
logger_dict["saved"] = True
self.full_save_path = backup_full_save_path
def save_extra(self, save_path):
self.env.save(save_path)
self.agent.save(save_path)
self.logger.save(save_path)
self.versioner.save(save_path)
rp.save(save_path)
def old_play(self, test_params=None, reward_from_agent=True):
rp.report("\n\n> Playing")
self.logger = Logger(
self.max_test_episodes,
self.agent.__class__.__name__,
self.agent.model.__class__.__name__,
self.agent.model,
self.agent.action_wrapper.__class__.__name__,
self.agent.action_wrapper.get_action_space_dim(),
self.agent.action_wrapper.get_named_actions(),
self.agent.state_builder.__class__.__name__,
self.agent.reward_builder.__class__.__name__,
self.env.__class__.__name__,
log_actions=self.log_actions,
episode_batch_avg_calculation=self.episode_batch_avg_calculation,
rolling_avg_window_size=self.rolling_avg_window_size)
while self.curr_playing_episodes < self.max_test_episodes:
self.curr_playing_episodes += 1
self.env.start()
# Reset the environment
obs = self.env.reset()
step_reward = 0
done = False
# Passing the episode to the agent reset, so that it can be passed to model reset
# Allowing the model to track the episode number, and decide if it should diminish the
# Learning Rate, depending on the currently selected strategy.
self.agent.reset(self.curr_playing_episodes)
ep_reward = 0
victory = False
ep_actions = np.zeros(
self.agent.action_wrapper.get_action_space_dim())
self.logger.record_episode_start()
for step in range(self.max_steps_testing):
action = self.agent.step(obs, done, is_testing=True)
# Take the action (a) and observe the outcome state(s') and reward (r)
obs, default_reward, done = self.env.step(action)
is_last_step = step == self.max_steps_testing - 1
done = done or is_last_step
if reward_from_agent:
step_reward = self.agent.get_reward(
obs, default_reward, done)
else:
step_reward = default_reward
ep_reward += step_reward
ep_actions[self.agent.previous_action] += 1
# If done: finish episode
if done:
victory = default_reward == 1
agent_info = {
"Learning rate": self.agent.model.learning_rate,
"Gamma": self.agent.model.gamma,
"Epsilon": self.agent.model.epsilon_greedy,
}
self.logger.record_episode(ep_reward, victory, step + 1,
agent_info, ep_actions)
break
self.logger.log_ep_stats()
if test_params != None:
test_params.logger.record_play_test(test_params.current_ep_count,
self.logger.ep_rewards,
self.logger.victories,
self.max_test_episodes)
else:
# Only logs train stats if this is not a test, to avoid cluttering the interface with info
self.logger.log_train_stats()
# We need to save playing status as well
if self.enable_save:
self.logger.save(self.full_save_play_path)
rp.save(self.full_save_play_path)