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63 lines
3.0 KiB
Python
63 lines
3.0 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.value_optimization_agent import *
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# Quantile Regression Deep Q Network - https://arxiv.org/pdf/1710.10044v1.pdf
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class QuantileRegressionDQNAgent(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.quantile_probabilities = np.ones(self.tp.agent.atoms) / float(self.tp.agent.atoms)
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# prediction's format is (batch,actions,atoms)
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def get_q_values(self, quantile_values):
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return np.dot(quantile_values, self.quantile_probabilities)
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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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# get the quantiles of the next states and current states
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next_state_quantiles = self.main_network.target_network.predict(next_states)
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current_quantiles = self.main_network.online_network.predict(current_states)
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# get the optimal actions to take for the next states
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target_actions = np.argmax(self.get_q_values(next_state_quantiles), axis=1)
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# calculate the Bellman update
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batch_idx = list(range(self.tp.batch_size))
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rewards = np.expand_dims(rewards, -1)
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game_overs = np.expand_dims(game_overs, -1)
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TD_targets = rewards + (1.0 - game_overs) * self.tp.agent.discount \
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* next_state_quantiles[batch_idx, target_actions]
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# get the locations of the selected actions within the batch for indexing purposes
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actions_locations = [[b, a] for b, a in zip(batch_idx, actions)]
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# calculate the cumulative quantile probabilities and reorder them to fit the sorted quantiles order
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cumulative_probabilities = np.array(range(self.tp.agent.atoms+1))/float(self.tp.agent.atoms) # tau_i
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quantile_midpoints = 0.5*(cumulative_probabilities[1:] + cumulative_probabilities[:-1]) # tau^hat_i
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quantile_midpoints = np.tile(quantile_midpoints, (self.tp.batch_size, 1))
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for idx in range(self.tp.batch_size):
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quantile_midpoints[idx, :] = quantile_midpoints[idx, np.argsort(current_quantiles[batch_idx, actions])[idx]]
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# train
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result = self.main_network.train_and_sync_networks([current_states, actions_locations, quantile_midpoints], TD_targets)
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total_loss = result[0]
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return total_loss
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