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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
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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import numpy as np
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from agents import agent
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from architectures import network_wrapper as nw
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import utils
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# Direct Future Prediction Agent - http://vladlen.info/papers/learning-to-act.pdf
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class DFPAgent(agent.Agent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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agent.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 = nw.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.append(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(current_states, 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=utils.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([observation, measurements, 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 == 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_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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