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This article reports on the reinforcement-learning competitions, which have been held annually since 2006. In these events, researchers from around the world developed reinforcement-learning agents to compete in domains of various complexity and difficulty. We focus on the 2008 competition, which employed fundamentally redesigned evaluation frameworks that 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 events, and outline ideas for the future of the competition.
@article{competitionreport08, Author = {Shimon Whiteson and Brian Tanner and Adam White}, Journal = {AI Magazine}, Month = {Summer}, Number = {2}, Pages = {81-94}, Title = {The Reinforcement Learning Competitions}, Volume = {31}, Year = {2010}}