A reinforcement learning approach to synthesizing climbing movements
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A4 Artikkeli konferenssijulkaisussa
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en
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7
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IEEE Conference on Games 2019, CoG 2019, IEEE Conference on Computatonal Intelligence and Games ; Volume 2019-August
Abstract
This paper addresses the problem of synthesizing simulated humanoid climbing movements given the target holds, e.g., by the player of a climbing game. We contribute the first deep reinforcement learning solution that can handle interactive physically simulated humanoid climbing with more than one limb switching holds at the same time. A key component of our approach is Self-Supervised Episode State Initialization (SS- ESI), which ensures diverse exploration and speeds up learning, compared to a baseline approach where the climber is reset to an initial pose after failure. Our results also show that training with a multi-step action parameterization can produce both smoother movements and enable learning from slightly fewer explored actions at the cost of increased simulation time per action.Description
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Naderi, K, Babadi, A, Roohi, S & Hamalainen, P 2019, A reinforcement learning approach to synthesizing climbing movements. in IEEE Conference on Games 2019, CoG 2019., 8848127, IEEE Conference on Computatonal Intelligence and Games, vol. 2019-August, IEEE, IEEE Conference on Games, London, United Kingdom, 20/08/2019. https://doi.org/10.1109/CIG.2019.8848127