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Many open problems involve the search for a mapping that is used by an algorithm solving an MDP. Useful mappings are often from the state set to some other set. Examples include representation discovery (a mapping to a feature space) and skill discovery (a mapping to skill termination probabilities). Different mappings result in algorithms achieving varying expected returns. In this paper we present a novel approach to the search for any mapping used by any algorithm attempting to solve an MDP, for that which results in maximum expected return.

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