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82 lines
3.6 KiB
Python
82 lines
3.6 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 numpy as np
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from agents.agent import *
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class ValueOptimizationAgent(Agent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0, create_target_network=True):
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Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
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self.main_network = NetworkWrapper(tuning_parameters, create_target_network, self.has_global, 'main',
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self.replicated_device, self.worker_device)
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self.networks.append(self.main_network)
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self.q_values = 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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# Algorithms for which q_values are calculated from predictions will override this function
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def get_q_values(self, prediction):
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return prediction
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def tf_input_state(self, curr_state):
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"""
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convert curr_state into input tensors tensorflow is expecting.
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TODO: move this function into Agent and use in as many agent implementations as possible
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currently, other agents will likely not work with environment measurements.
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This will become even more important as we support more complex and varied environment states.
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"""
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# convert to batch so we can run it through the network
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observation = np.expand_dims(np.array(curr_state['observation']), 0)
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if self.tp.agent.use_measurements:
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measurements = np.expand_dims(np.array(curr_state['measurements']), 0)
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tf_input_state = [observation, measurements]
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else:
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tf_input_state = observation
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return tf_input_state
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def get_prediction(self, curr_state):
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return self.main_network.online_network.predict(self.tf_input_state(curr_state))
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def choose_action(self, curr_state, phase=RunPhase.TRAIN):
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prediction = self.get_prediction(curr_state)
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actions_q_values = self.get_q_values(prediction)
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# choose action according to the exploration policy and the current phase (evaluating or training the agent)
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if phase == RunPhase.TRAIN:
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action = self.exploration_policy.get_action(actions_q_values)
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else:
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action = self.evaluation_exploration_policy.get_action(actions_q_values)
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# this is for bootstrapped dqn
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if type(actions_q_values) == list and len(actions_q_values) > 0:
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actions_q_values = actions_q_values[self.exploration_policy.selected_head]
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actions_q_values = actions_q_values.squeeze()
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# store the q values statistics for logging
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self.q_values.add_sample(actions_q_values)
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# store information for plotting interactively (actual plotting is done in agent)
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if self.tp.visualization.plot_action_values_online:
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for idx, action_name in enumerate(self.env.actions_description):
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self.episode_running_info[action_name].append(actions_q_values[idx])
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action_value = {"action_value": actions_q_values[action]}
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return action, action_value
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