Research ArticleARTIFICIAL INTELLIGENCE

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

See allHide authors and affiliations

Science Robotics  23 Sep 2020:
Vol. 5, Issue 46, eabb9764
DOI: 10.1126/scirobotics.abb9764

You are currently viewing the abstract.

View Full Text

Log in to view the full text

Log in through your institution

Log in through your institution

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.

View Full Text

Stay Connected to Science Robotics