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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
116 lines
5.2 KiB
Python
116 lines
5.2 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 copy
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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 agent
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from architectures import network_wrapper as nw
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import configurations as conf
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import utils
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# Deep Deterministic Policy Gradients Network - https://arxiv.org/pdf/1509.02971.pdf
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class DDPGAgent(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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# define critic network
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self.critic_network = self.main_network
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# self.networks.append(self.critic_network)
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# define actor network
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tuning_parameters.agent.input_types = {'observation': conf.InputTypes.Observation}
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tuning_parameters.agent.output_types = [conf.OutputTypes.Pi]
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self.actor_network = nw.NetworkWrapper(tuning_parameters, True, self.has_global, 'actor',
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self.replicated_device, self.worker_device)
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self.networks.append(self.actor_network)
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self.q_values = utils.Signal("Q")
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self.signals.append(self.q_values)
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self.reset_game(do_not_reset_env=True)
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def learn_from_batch(self, batch):
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current_states, next_states, actions, rewards, game_overs, _ = self.extract_batch(batch)
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# TD error = r + discount*max(q_st_plus_1) - q_st
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next_actions = self.actor_network.target_network.predict(next_states)
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inputs = copy.copy(next_states)
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inputs['action'] = next_actions
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q_st_plus_1 = self.critic_network.target_network.predict(inputs)
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TD_targets = np.expand_dims(rewards, -1) + \
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(1.0 - np.expand_dims(game_overs, -1)) * self.tp.agent.discount * q_st_plus_1
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# get the gradients of the critic output with respect to the action
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actions_mean = self.actor_network.online_network.predict(current_states)
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critic_online_network = self.critic_network.online_network
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# TODO: convert into call to predict, current method ignores lstm middleware for example
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action_gradients = self.critic_network.sess.run(critic_online_network.gradients_wrt_inputs['action'],
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feed_dict=critic_online_network._feed_dict({
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**current_states,
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'action': actions_mean,
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}))[0]
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# train the critic
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if len(actions.shape) == 1:
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actions = np.expand_dims(actions, -1)
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result = self.critic_network.train_and_sync_networks({**current_states, 'action': actions}, TD_targets)
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total_loss = result[0]
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# apply the gradients from the critic to the actor
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actor_online_network = self.actor_network.online_network
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gradients = self.actor_network.sess.run(actor_online_network.weighted_gradients,
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feed_dict=actor_online_network._feed_dict({
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**current_states,
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actor_online_network.gradients_weights_ph: -action_gradients,
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}))
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if self.actor_network.has_global:
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self.actor_network.global_network.apply_gradients(gradients)
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self.actor_network.update_online_network()
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else:
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self.actor_network.online_network.apply_gradients(gradients)
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return total_loss
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def train(self):
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return agent.Agent.train(self)
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def choose_action(self, curr_state, phase=utils.RunPhase.TRAIN):
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assert not self.env.discrete_controls, 'DDPG works only for continuous control problems'
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result = self.actor_network.online_network.predict(self.tf_input_state(curr_state))
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action_values = result[0].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 = action_values
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action = np.clip(action, self.env.action_space_low, self.env.action_space_high)
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# get q value
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action_batch = np.expand_dims(action, 0)
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if type(action) != np.ndarray:
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action_batch = np.array([[action]])
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inputs = self.tf_input_state(curr_state)
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inputs['action'] = action_batch
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q_value = self.critic_network.online_network.predict(inputs)[0]
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self.q_values.add_sample(q_value)
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action_info = {"action_value": q_value}
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return action, action_info
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