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An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions

Authors
Won, Dong-OkMueller, Klaus-RobertLee, Seong-Whan
Issue Date
30-9월-2020
Publisher
AMER ASSOC ADVANCEMENT SCIENCE
Citation
SCIENCE ROBOTICS, v.5, no.46
Indexed
SCIE
SCOPUS
Journal Title
SCIENCE ROBOTICS
Volume
5
Number
46
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/53093
DOI
10.1126/scirobotics.abb9764
ISSN
2470-9476
Abstract
The game of curling can be considered a good test bed for studying the interaction between artificial intelligence systems and the real world. In curling, the environmental characteristics change at every moment, and every throw has an impact on the outcome of the match. Furthermore, there is no time for relearning during a curling match due to the timing rules of the game. Here, we report a curling robot that can achieve human-level performance in the game of curling using an adaptive deep reinforcement learning framework. Our proposed adaptation framework extends standard deep reinforcement learning using temporal features, which learn to compensate for the uncertainties and nonstationarities that are an unavoidable part of curling. Our curling robot, Curly, was able to win three of four official matches against expert human teams [top-ranked women's curling teams and Korea national wheelchair curling team (reserve team)]. These results indicate that the gap between physics-based simulators and the real world can be narrowed.
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