Report on the 2008 Reinforcement Learning Competition
Abstract
This article reports on the 2008 Reinforcement Learning Competition,
which began in November 2007 and ended with a workshop at the
International Conference on Machine Learning (ICML) in July 2008 in
Helsinki, Finland. Researchers from around the world developed
reinforcement learning agents to compete in six problems of various
complexity and difficulty. The competition 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 also 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 and present a summary of selected competition
results. Finally, we discuss the implications of these results and
outline ideas for the future of the competition.
which began in November 2007 and ended with a workshop at the
International Conference on Machine Learning (ICML) in July 2008 in
Helsinki, Finland. Researchers from around the world developed
reinforcement learning agents to compete in six problems of various
complexity and difficulty. The competition 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 also 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 and present a summary of selected competition
results. Finally, we discuss the implications of these results and
outline ideas for the future of the competition.
Keywords
reinforcement learning
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