Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/123724
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Type: Conference paper
Title: Reinforcement learning with attention that works: a self-supervised approach
Author: Manchin, A.
Abbasnejad, E.
Van Den Hengel, A.
Citation: Neural Information Processing: 26th International Conference, ICONIP 2019. Proceedings, Part V, 2019 / Gedeon, T., Wong, K.W., Lee, M. (ed./s), vol.1143 CCIS, pp.223-230
Publisher: Springer
Publisher Place: Switzerland
Issue Date: 2019
Series/Report no.: Communications in Computer and Information Science; 1143
ISBN: 9783030368012
ISSN: 1865-0929
1865-0937
Conference Name: International Conference on Neural Information Processing (ICONIP) (12 Dec 2019 - 15 Dec 2019 : Sydney, Australia)
Statement of
Responsibility: 
Anthony Manchin, Ehsan Abbasnejad, and Anton van den Hengel
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.
Keywords: Reinforcement learning; Attention; Deep learning
Rights: © Springer Nature Switzerland AG 2019
RMID: 1000013794
DOI: 10.1007/978-3-030-36802-9_25
Published version: https://doi.org/10.1007/978-3-030-36802-9
Appears in Collections:Australian Institute for Machine Learning publications

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