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Stochastic decision processes in reinforcement learning are usually formulated as Markov decision processes which are stationary and ergodic. However, in fact, some of the stochastic decision processes are not necessarily Markov, stationary, and/or ergodic. In this paper, using an information-theoretic property, we show a class of stochastic decision processes in reinforcement learning in which return...
We introduce novel dissimilarity into a probabilistic clustering task to properly measure dissimilarity among multiple clusters when each cluster is characterized by a subpopulation in the mixture model. This measure of dissimilarity is called redundancy-based dissimilarity among probability distributions. From aspects of both source coding and a statistical hypothesis test, we shed light on several...
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