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We propose a partial-information state based approach to the optimization of the long-run average performance in a partially observable Markov decision process (POMDP). In this approach, the information history is summarized (at least partially) by a (or a few) statistic(s), not necessary sufficient, called a partial-information state, and actions depend on the partial-information state, rather than...
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