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This study is concerned with finite Markov decision processes (MDPs) whose state are exactly observable but its transition matrix is unknown. We develop a learning algorithm of the reward-penalty type... for the communicating case of multi-chain MDPs. An adaptively optimal policy and an asymptotic sequence of adaptive policies with nearly optimal properties are constructed under the average expected reward criterion. Also, a numerical experiment is given to show the practical effectiveness of the algorithm.show more
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bic039_p011