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Action set selection in Markov Decision Processes (MDPs) is an area of research that has received little attention. On the other hand, the set of actions available to an MDP agent can have a significant impact on the ability of the agent to gain optimal rewards. Last year at GECCO'05, the first automated action set selection tool powered by genetic algorithms was presented. The demonstration of its capabilities, though intriguing, was limited to a single domain. In this paper, we apply the tooldoi:10.1145/1143997.1144275 dblp:conf/gecco/LeeB06 fatcat:a7vscboub5ekloosizxa5aag54