Convex analytic approach to constrained discounted Markov decision processes with non-constant discount factors

DOI10.1007/s11750-011-0186-8zbMath1273.90235OpenAlexW2058577181MaRDI QIDQ356522

Yi Zhang

Publication date: 26 July 2013

Published in: Top (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11750-011-0186-8

zbMATH Keywords

Markov decision processesoccupation measureconvex analytic approach

Mathematics Subject Classification ID

Discrete-time Markov processes on general state spaces (60J05) Markov and semi-Markov decision processes (90C40)

Related Items (7)

Discrete-time zero-sum Markov games with first passage criteria ⋮ Two-person zero-sum stochastic games with varying discount factors ⋮ Extreme Occupation Measures in Markov Decision Processes with an Absorbing State ⋮ Constrained discounted Markov decision processes with Borel state spaces ⋮ Convergence of Markov decision processes with constraints and state-action dependent discount factors ⋮ Semi-Markov decision processes with variance minimization criterion ⋮ First passage Markov decision processes with constraints and varying discount factors

Cites Work

• A convex analytic approach to Markov decision processes
• Stochastic optimal control. The discrete time case
• Constrained Markov control processes in Borel spaces: the discounted case
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• Discounted Continuous-Time Markov Decision Processes with Constraints: Unbounded Transition and Loss Rates
• Conditions for optimality in dynamic programming and for the limit of n-stage optimal policies to be optimal
• Controlled random sequences: methods of convex analysis and problems with functional constraints
• Strategic measures in optimal control problems for stochastic sequences
• On Optimal Strategies in Control Problems with Constraints
• Average Optimality in Markov Control Processes via Discounted-Cost Problems and Linear Programming
• Dynamic Programming Equations for Discounted Constrained Stochastic Control
• Extreme Points of Sets of Randomized Strategies in Constrained Optimization and Control Problems
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