Regular competitive events have helped drive research progress and further the state-of-the-art in many areas, such as: data mining (KDD Cup), planning and scheduling (International Planning Competition) and multi-agent robotics (Robocup). Over the past two years the reinforcement learning community has held two competitive meetings. This workshop will be the culmination of the First Annual Reinforcement Learning Competition. The competition will use the same evaluation software and similar problem sets as the previous competitions, but also include several new events. This competition will feature a pentathlon: the agent that achieves the best performance across all five domains will be declared the winner. The winners will be invited to describe their approach at the workshop, with the aim of improving the participants' expertise on applying reinforcement learning techniques in practice.
Adam M White (University of Alberta)
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