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Article overview
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Deep Attention Recurrent Q-Network | Ivan Sorokin
; Alexey Seleznev
; Mikhail Pavlov
; Aleksandr Fedorov
; Anastasiia Ignateva
; | Date: |
5 Dec 2015 | Abstract: | A deep learning approach to reinforcement learning led to a general learner
able to train on visual input to play a variety of arcade games at the human
and superhuman levels. Its creators at the Google DeepMind’s team called the
approach: Deep Q-Network (DQN). We present an extension of DQN by "soft" and
"hard" attention mechanisms. Tests of the proposed Deep Attention Recurrent
Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of
performance superior to that of DQN. Moreover, built-in attention mechanisms
allow a direct online monitoring of the training process by highlighting the
regions of the game screen the agent is focusing on when making decisions. | Source: | arXiv, 1512.1693 | Services: | Forum | Review | PDF | Favorites |
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