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