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|Title:||Reinforcement learning with attention that works: a self-supervised approach|
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|
|Series/Report no.:||Communications in Computer and Information Science; 1143|
|Conference Name:||International Conference on Neural Information Processing (ICONIP) (12 Dec 2019 - 15 Dec 2019 : Sydney, Australia)|
|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|
|Appears in Collections:||Australian Institute for Machine Learning publications|
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