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87 lines
4.0 KiB
Python
87 lines
4.0 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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from agents.agent import *
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# Direct Future Prediction Agent - http://vladlen.info/papers/learning-to-act.pdf
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class DFPAgent(Agent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
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self.current_goal = self.tp.agent.goal_vector
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self.main_network = NetworkWrapper(tuning_parameters, False, 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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def learn_from_batch(self, batch):
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current_states, next_states, actions, rewards, game_overs, total_returns = self.extract_batch(batch)
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# create the inputs for the network
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input = current_states
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input['goal'] = np.repeat(np.expand_dims(self.current_goal, 0), self.tp.batch_size, 0)
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# get the current outputs of the network
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targets = self.main_network.online_network.predict(input)
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# change the targets for the taken actions
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for i in range(self.tp.batch_size):
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targets[i, actions[i]] = batch[i].info['future_measurements'].flatten()
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result = self.main_network.train_and_sync_networks(input, targets)
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total_loss = result[0]
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return total_loss
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def choose_action(self, curr_state, phase=RunPhase.TRAIN):
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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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measurements = np.expand_dims(np.array(curr_state['measurements']), 0)
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goal = np.expand_dims(self.current_goal, 0)
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# predict the future measurements
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measurements_future_prediction = self.main_network.online_network.predict({
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"observation": observation,
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"measurements": measurements,
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"goal": goal})[0]
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action_values = np.zeros((self.action_space_size,))
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num_steps_used_for_objective = len(self.tp.agent.future_measurements_weights)
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# calculate the score of each action by multiplying it's future measurements with the goal vector
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for action_idx in range(self.action_space_size):
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action_measurements = measurements_future_prediction[action_idx]
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action_measurements = np.reshape(action_measurements,
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(self.tp.agent.num_predicted_steps_ahead, self.measurements_size[0]))
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future_steps_values = np.dot(action_measurements, self.current_goal)
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action_values[action_idx] = np.dot(future_steps_values[-num_steps_used_for_objective:],
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self.tp.agent.future_measurements_weights)
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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(action_values)
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else:
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action = np.argmax(action_values)
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action_values = action_values.squeeze()
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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(action_values[idx])
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action_info = {"action_probability": 0, "action_value": action_values[action]}
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return action, action_info
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