The Reinforcement Learning Competitions

Abstract

This article reports on the Reinforcement Learning Competitions, which have been held annually since 2007. In these events, researchers from around the world developed reinforcement learning agents to compete in tasks of various complexity and difficulty. We focus on the 2008 competition, which employed fundamentally redesigned evaluation frameworks that, unlike those in previous competitions, aimed to systematically encourage the submission of robust learning methods. We describe the unique challenges of empirical evaluation in reinforcement learning and briefly review the history of the previous competitions and the evaluation frameworks they employed. We describe the novel frameworks developed for the 2008 competition as well as the software infrastructure on which they rely. Furthermore, we describe the six competition domains, present selected competition results, and discuss the implications of these results. Finally, we summarize the 2009 competition, which used the same evaluation framework but different event, and outline ideas for the future of the competition.