Journal
SCIENCE ROBOTICS
Volume 5, Issue 46, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/scirobotics.abb9764
Keywords
-
Categories
Funding
- Institute for Information & Communications Technology Planning & Evaluation (IITP) - Korea government [2017-0-00521, 2017-0-01779, 2019-0-00079]
- Microsoft Research Asia
- German Ministry for Education and Research (BMBF) [01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, 01IS18037A]
- German Research Foundation (DFG) [EXC 2046/1, 390685689]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available