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66 lines
3.1 KiB
Python
66 lines
3.1 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.value_optimization_agent import *
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# Persistent Advantage Learning - https://arxiv.org/pdf/1512.04860.pdf
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class PALAgent(ValueOptimizationAgent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
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self.alpha = tuning_parameters.agent.pal_alpha
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self.persistent = tuning_parameters.agent.persistent_advantage_learning
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self.monte_carlo_mixing_rate = tuning_parameters.agent.monte_carlo_mixing_rate
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def learn_from_batch(self, batch):
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current_states, next_states, actions, rewards, game_overs, total_return = self.extract_batch(batch)
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selected_actions = np.argmax(self.main_network.online_network.predict(next_states), 1)
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# next state values
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q_st_plus_1_target = self.main_network.target_network.predict(next_states)
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v_st_plus_1_target = np.max(q_st_plus_1_target, 1)
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# current state values according to online network
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q_st_online = self.main_network.online_network.predict(current_states)
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# current state values according to target network
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q_st_target = self.main_network.target_network.predict(current_states)
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v_st_target = np.max(q_st_target, 1)
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# calculate TD error
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TD_targets = np.copy(q_st_online)
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for i in range(self.tp.batch_size):
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TD_targets[i, actions[i]] = rewards[i] + (1.0 - game_overs[i]) * self.tp.agent.discount * \
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q_st_plus_1_target[i][selected_actions[i]]
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advantage_learning_update = v_st_target[i] - q_st_target[i, actions[i]]
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next_advantage_learning_update = v_st_plus_1_target[i] - q_st_plus_1_target[i, selected_actions[i]]
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# Persistent Advantage Learning or Regular Advantage Learning
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if self.persistent:
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TD_targets[i, actions[i]] -= self.alpha * min(advantage_learning_update, next_advantage_learning_update)
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else:
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TD_targets[i, actions[i]] -= self.alpha * advantage_learning_update
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# mixing monte carlo updates
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monte_carlo_target = total_return[i]
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TD_targets[i, actions[i]] = (1 - self.monte_carlo_mixing_rate) * TD_targets[i, actions[i]] \
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+ self.monte_carlo_mixing_rate * monte_carlo_target
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result = self.main_network.train_and_sync_networks(current_states, TD_targets)
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total_loss = result[0]
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return total_loss
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