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Papers/Deep Attention Recurrent Q-Network

Deep Attention Recurrent Q-Network

Ivan Sorokin, Alexey Seleznev, Mikhail Pavlov, Aleksandr Fedorov, Anastasiia Ignateva

2015-12-05Reinforcement LearningDeep AttentionAtari Gamesreinforcement-learning
PaperPDFCodeCode(official)Code

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.

Results

TaskDatasetMetricValueModel
Atari GamesAtari 2600 TutankhamScore197DARQN soft
Atari GamesAtari 2600 BreakoutScore20DARQN hard
Atari GamesAtari 2600 GopherScore5356DARQN soft
Atari GamesAtari 2600 Space InvadersScore650DARQN soft
Atari GamesAtari 2600 SeaquestScore7263DARQN soft
Video GamesAtari 2600 TutankhamScore197DARQN soft
Video GamesAtari 2600 BreakoutScore20DARQN hard
Video GamesAtari 2600 GopherScore5356DARQN soft
Video GamesAtari 2600 Space InvadersScore650DARQN soft
Video GamesAtari 2600 SeaquestScore7263DARQN soft

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