Reinforcement learning with attention that works: a self-supervised approach
Date
2019
Authors
Manchin, A.
Abbasnejad, E.
Van Den Hengel, A.
Editors
Gedeon, T.
Wong, K.W.
Lee, M.
Wong, K.W.
Lee, M.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Communications in Computer and Information Science, 2019 / Gedeon, T., Wong, K.W., Lee, M. (ed./s), vol.1143 CCIS, pp.223-230
Statement of Responsibility
Anthony Manchin, Ehsan Abbasnejad, and Anton van den Hengel
Conference Name
International Conference on Neural Information Processing (ICONIP) (12 Dec 2019 - 15 Dec 2019 : Sydney, Australia)
Abstract
Attention models have had a significant positive impact on deep learning across a range of tasks. However previous attempts at integrating attention with reinforcement learning have failed to produce significant improvements. Unlike the selective attention models used in previous attempts, which constrain the attention via preconceived notions of importance, our implementation utilises the Markovian properties inherent in the state input. We propose the first combination of self attention and reinforcement learning that is capable of producing significant improvements, including new state of the art results in the Arcade Learning Environment.
School/Discipline
Dissertation Note
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Description
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Rights
© Springer Nature Switzerland AG 2019