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59 lines
2.6 KiB
Python
59 lines
2.6 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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# Bootstrapped DQN - https://arxiv.org/pdf/1602.04621.pdf
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class BootstrappedDQNAgent(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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def reset_game(self, do_not_reset_env=False):
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ValueOptimizationAgent.reset_game(self, do_not_reset_env)
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self.exploration_policy.select_head()
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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:
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# TD error = r + discount*max(q_st_plus_1) - q_st
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# for all other actions, the error is 0
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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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for i in range(self.tp.batch_size):
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mask = batch[i].info['mask']
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for head_idx in range(self.tp.exploration.architecture_num_q_heads):
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if mask[head_idx] == 1:
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TD_targets[head_idx][i, actions[i]] = rewards[i] + \
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(1.0 - game_overs[i]) * self.tp.agent.discount * np.max(
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q_st_plus_1[head_idx][i], 0)
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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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def act(self, phase=RunPhase.TRAIN):
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ValueOptimizationAgent.act(self, phase)
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mask = np.random.binomial(1, self.tp.exploration.bootstrapped_data_sharing_probability,
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self.tp.exploration.architecture_num_q_heads)
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self.memory.update_last_transition_info({'mask': mask})
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