A reinforcement learning approach to synthesizing climbing movements

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Conference article in proceedings
Date
2019-08-01
Major/Subject
Mcode
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Language
en
Pages
7
Series
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.
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Keywords
Action parameterization, Climbing movements, Reinforcement learning, State initialization
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Citation
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