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2002-
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SNF
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General Methods for Search and Reinforcement Learning
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We focus on computable, practically feasible optimal search and learning algorithms based on concepts of algorithmic information theory, using principles of Levin's nonincremental universal search and Schmidhuber's very recent, incremental, optimal ordered problem solver (OOPS) for problems of optimization and prediction. OOPS is a novel, optimally fast, incremental learner that is able to improve itself through experience. It already has been shown to solve tasks on which traditional search and learning algorithms fail. It is now being applied to to problems of game-playing and robotics.
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Matteo Gagliolo
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Coordinator
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Juergen Schmidhuber
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