NON-DISCOUNTED OPTIMAL POLICIES IN CONTROLLED MARKOV SET-CHAINS

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概要

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• In a controlled Markov set-chain with a discount factor β, we consider the case of β = 1 as a limiting case of β < 1 and find a non-discounted optimal policy which maximizes Abel-sum of rewards in time over all stationary policies under some partial order. We analyze the behavior of discounted total rewards as discount factor β approaches 1 under regularity conditions, and prove the existence of a non-discounted optimal policy, applying the Kakutani's fixed point theorem and policy improvement method. As a numerical example the Toymaker's problem is considered.

• 社団法人日本オペレーションズ・リサーチ学会の論文

著者

• Kurano M

  千葉大学教育学部

• Kurano Masami

  Chiba University

• Hosaka Masanori

  Chiba University

関連論文

• Fuzzy Perceptive Values for MDPs with Discounting (Mathematical Theory and Applications of Uncertainty Sciences and Decision Making)
• ORDERING OF CONVEX FUZZY SETS : A BRIEF SURVEY AND NEW RESULTS
• NON-DISCOUNTED OPTIMAL POLICIES IN CONTROLLED MARKOV SET-CHAINS

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