Reinforcement learning with attention that works: a self-supervised approach

Manchin, A.Abbasnejad, E.Van Den Hengel, A.Gedeon, T.Wong, K.W.Lee, M.2020-03-182020-03-182019Communications in Computer and Information Science, 2019 / Gedeon, T., Wong, K.W., Lee, M. (ed./s), vol.1143 CCIS, pp.223-23097830303680121865-09291865-0937http://hdl.handle.net/2440/123724Attention 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.en© Springer Nature Switzerland AG 2019Reinforcement learning; Attention; Deep learningReinforcement learning with attention that works: a self-supervised approachConference paper100001379410.1007/978-3-030-36802-9_250006327597000252-s2.0-85078473236516272Van Den Hengel, A. [0000-0003-3027-8364]