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1b095aeeca
Till now, most of the modules were importing all of the module objects (variables, classes, functions, other imports) into module namespace, which potentially could (and was) cause of unintentional use of class or methods, which was indirect imported. With this patch, all the star imports were substituted with top-level module, which provides desired class or function. Besides, all imports where sorted (where possible) in a way pep8[1] suggests - first are imports from standard library, than goes third party imports (like numpy, tensorflow etc) and finally coach modules. All of those sections are separated by one empty line. [1] https://www.python.org/dev/peps/pep-0008/#imports
220 lines
10 KiB
Python
220 lines
10 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import collections
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import copy
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from random import shuffle
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import numpy as np
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from agents import actor_critic_agent as aca
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from agents import policy_optimization_agent as poa
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import logger
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import utils
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# Clipped Proximal Policy Optimization - https://arxiv.org/abs/1707.06347
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class ClippedPPOAgent(aca.ActorCriticAgent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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aca.ActorCriticAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id,
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create_target_network=True)
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# signals definition
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self.value_loss = utils.Signal('Value Loss')
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self.signals.append(self.value_loss)
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self.policy_loss = utils.Signal('Policy Loss')
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self.signals.append(self.policy_loss)
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self.total_kl_divergence_during_training_process = 0.0
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self.unclipped_grads = utils.Signal('Grads (unclipped)')
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self.signals.append(self.unclipped_grads)
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self.value_targets = utils.Signal('Value Targets')
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self.signals.append(self.value_targets)
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self.kl_divergence = utils.Signal('KL Divergence')
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self.signals.append(self.kl_divergence)
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def fill_advantages(self, batch):
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current_states, next_states, actions, rewards, game_overs, total_return = self.extract_batch(batch)
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current_state_values = self.main_network.online_network.predict(current_states)[0]
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current_state_values = current_state_values.squeeze()
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self.state_values.add_sample(current_state_values)
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# calculate advantages
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advantages = []
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value_targets = []
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if self.policy_gradient_rescaler == poa.PolicyGradientRescaler.A_VALUE:
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advantages = total_return - current_state_values
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elif self.policy_gradient_rescaler == poa.PolicyGradientRescaler.GAE:
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# get bootstraps
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episode_start_idx = 0
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advantages = np.array([])
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value_targets = np.array([])
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for idx, game_over in enumerate(game_overs):
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if game_over:
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# get advantages for the rollout
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value_bootstrapping = np.zeros((1,))
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rollout_state_values = np.append(current_state_values[episode_start_idx:idx+1], value_bootstrapping)
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rollout_advantages, gae_based_value_targets = \
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self.get_general_advantage_estimation_values(rewards[episode_start_idx:idx+1],
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rollout_state_values)
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episode_start_idx = idx + 1
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advantages = np.append(advantages, rollout_advantages)
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value_targets = np.append(value_targets, gae_based_value_targets)
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else:
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logger.screen.warning("WARNING: The requested policy gradient rescaler is not available")
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# standardize
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advantages = (advantages - np.mean(advantages)) / np.std(advantages)
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for transition, advantage, value_target in zip(batch, advantages, value_targets):
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transition.info['advantage'] = advantage
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transition.info['gae_based_value_target'] = value_target
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self.action_advantages.add_sample(advantages)
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def train_network(self, dataset, epochs):
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loss = []
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for j in range(epochs):
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loss = {
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'total_loss': [],
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'policy_losses': [],
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'unclipped_grads': [],
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'fetch_result': []
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}
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shuffle(dataset)
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for i in range(int(len(dataset) / self.tp.batch_size)):
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batch = dataset[i * self.tp.batch_size:(i + 1) * self.tp.batch_size]
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current_states, _, actions, _, _, total_return = self.extract_batch(batch)
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advantages = np.array([t.info['advantage'] for t in batch])
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gae_based_value_targets = np.array([t.info['gae_based_value_target'] for t in batch])
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if not self.tp.env_instance.discrete_controls and len(actions.shape) == 1:
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actions = np.expand_dims(actions, -1)
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# get old policy probabilities and distribution
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result = self.main_network.target_network.predict(current_states)
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old_policy_distribution = result[1:]
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# calculate gradients and apply on both the local policy network and on the global policy network
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fetches = [self.main_network.online_network.output_heads[1].kl_divergence,
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self.main_network.online_network.output_heads[1].entropy]
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total_return = np.expand_dims(total_return, -1)
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value_targets = gae_based_value_targets if self.tp.agent.estimate_value_using_gae else total_return
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inputs = copy.copy(current_states)
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# TODO: why is this output 0 and not output 1?
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inputs['output_0_0'] = actions
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# TODO: does old_policy_distribution really need to be represented as a list?
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# A: yes it does, in the event of discrete controls, it has just a mean
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# otherwise, it has both a mean and standard deviation
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for input_index, input in enumerate(old_policy_distribution):
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inputs['output_0_{}'.format(input_index + 1)] = input
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total_loss, policy_losses, unclipped_grads, fetch_result =\
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self.main_network.online_network.accumulate_gradients(
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inputs, [total_return, advantages], additional_fetches=fetches)
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self.value_targets.add_sample(value_targets)
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if self.tp.distributed:
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self.main_network.apply_gradients_to_global_network()
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self.main_network.update_online_network()
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else:
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self.main_network.apply_gradients_to_online_network()
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self.main_network.online_network.reset_accumulated_gradients()
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loss['total_loss'].append(total_loss)
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loss['policy_losses'].append(policy_losses)
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loss['unclipped_grads'].append(unclipped_grads)
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loss['fetch_result'].append(fetch_result)
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self.unclipped_grads.add_sample(unclipped_grads)
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for key in loss.keys():
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loss[key] = np.mean(loss[key], 0)
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if self.tp.learning_rate_decay_rate != 0:
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curr_learning_rate = self.main_network.online_network.get_variable_value(self.tp.learning_rate)
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self.curr_learning_rate.add_sample(curr_learning_rate)
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else:
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curr_learning_rate = self.tp.learning_rate
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# log training parameters
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logger.screen.log_dict(
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collections.OrderedDict([
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("Surrogate loss", loss['policy_losses'][0]),
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("KL divergence", loss['fetch_result'][0]),
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("Entropy", loss['fetch_result'][1]),
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("training epoch", j),
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("learning_rate", curr_learning_rate)
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]),
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prefix="Policy training"
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)
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self.total_kl_divergence_during_training_process = loss['fetch_result'][0]
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self.entropy.add_sample(loss['fetch_result'][1])
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self.kl_divergence.add_sample(loss['fetch_result'][0])
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return policy_losses
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def post_training_commands(self):
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# clean memory
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self.memory.clean()
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def train(self):
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self.main_network.sync()
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dataset = self.memory.transitions
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self.fill_advantages(dataset)
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# take only the requested number of steps
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dataset = dataset[:self.tp.agent.num_consecutive_playing_steps]
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if self.tp.distributed and self.tp.agent.share_statistics_between_workers:
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self.running_observation_stats.push(np.array([t.state['observation'] for t in dataset]))
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losses = self.train_network(dataset, 10)
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self.value_loss.add_sample(losses[0])
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self.policy_loss.add_sample(losses[1])
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self.update_log() # should be done in order to update the data that has been accumulated * while not playing *
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return np.append(losses[0], losses[1])
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def choose_action(self, current_state, phase=utils.RunPhase.TRAIN):
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if self.env.discrete_controls:
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# DISCRETE
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_, action_values = self.main_network.online_network.predict(self.tf_input_state(current_state))
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action_values = action_values.squeeze()
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if phase == utils.RunPhase.TRAIN:
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action = self.exploration_policy.get_action(action_values)
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else:
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action = np.argmax(action_values)
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action_info = {"action_probability": action_values[action]}
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# self.entropy.add_sample(-np.sum(action_values * np.log(action_values)))
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else:
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# CONTINUOUS
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_, action_values_mean, action_values_std = self.main_network.online_network.predict(self.tf_input_state(current_state))
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action_values_mean = action_values_mean.squeeze()
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action_values_std = action_values_std.squeeze()
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if phase == utils.RunPhase.TRAIN:
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action = np.squeeze(np.random.randn(1, self.action_space_size) * action_values_std + action_values_mean)
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# if self.current_episode % 5 == 0 and self.current_episode_steps_counter < 5:
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# print action
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else:
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action = action_values_mean
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action_info = {"action_probability": action_values_mean}
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return action, action_info
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