Utility, probabilistic constraints, mean and variance of discounted rewards in Markov decision processes

DOI10.1007/BF01720793zbMath0622.90090MaRDI QIDQ1091281

Douglas J. White

Publication date: 1987

Published in: OR Spektrum (Search for Journal in Brave)

zbMATH Keywords

discounted rewardsdiscounted Markov decision processesaugmented state-spacesfinite state-space

Mathematics Subject Classification ID

Markov and semi-Markov decision processes (90C40)

Related Items (6)

Value iteration methods in risk minimizing stopping problems ⋮ Experiments with dynamic programming algorithms for nonseparable problems ⋮ Finding Optimal Survey Policies via Adaptive Markov Decision Processes ⋮ Non-homogeneous Markov decision processes with a constraint ⋮ Expected utility maximization of optimal stopping problems ⋮ Optimal policy for minimizing risk models in Markov decision processes

Cites Work

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• Non-Randomized Markov and Semi-Markov Strategies in Dynamic Programming
• Decision Problems with Expected Utility Critera, I: Upper and Lower Convergent Utility
• The variance of discounted Markov decision processes
• Technical Note—Dynamic Programming and Probabilistic Constraints
• Discounted Dynamic Programming
• Some Remarks on Finite Horizon Markovian Decision Models

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