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61 lines
2.9 KiB
Python
61 lines
2.9 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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# Distributional Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf
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class DistributionalDQNAgent(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.z_values = np.linspace(self.tp.agent.v_min, self.tp.agent.v_max, self.tp.agent.atoms)
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# prediction's format is (batch,actions,atoms)
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def get_q_values(self, prediction):
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return np.dot(prediction, self.z_values)
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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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# for the action we actually took, the error is calculated by the atoms distribution
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# for all other actions, the error is 0
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distributed_q_st_plus_1 = self.main_network.target_network.predict(next_states)
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# initialize with the current prediction so that we will
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TD_targets = self.main_network.online_network.predict(current_states)
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# only update the action that we have actually done in this transition
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target_actions = np.argmax(self.get_q_values(distributed_q_st_plus_1), axis=1)
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m = np.zeros((self.tp.batch_size, self.z_values.size))
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batches = np.arange(self.tp.batch_size)
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for j in range(self.z_values.size):
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tzj = np.fmax(np.fmin(rewards + (1.0 - game_overs) * self.tp.agent.discount * self.z_values[j],
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self.z_values[self.z_values.size - 1]),
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self.z_values[0])
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bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0])
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u = (np.ceil(bj)).astype(int)
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l = (np.floor(bj)).astype(int)
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m[batches, l] = m[batches, l] + (distributed_q_st_plus_1[batches, target_actions, j] * (u - bj))
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m[batches, u] = m[batches, u] + (distributed_q_st_plus_1[batches, target_actions, j] * (bj - l))
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# total_loss = cross entropy between actual result above and predicted result for the given action
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TD_targets[batches, actions] = m
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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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