Finite state approximation algorithms for average cost denumerable state Markov decision processes

DOI10.1007/BF01719758zbMath0578.90090OpenAlexW1968462099MaRDI QIDQ1066819

B. George

Publication date: 1985

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

Full work available at URL: https://doi.org/10.1007/bf01719758

zbMATH Keywords

convergenceapproximate solutionspolicy iterationvalue iterationaverage cost criteriainfinite state space Markov decision processes

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Markov and semi-Markov decision processes (90C40)

Related Items (3)

Asymptotic properties of constrained Markov Decision Processes ⋮ The blast furnaces problem ⋮ Probabilistic models for optimizing patients survival rates

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Finite-state approximations for denumerable-state infinite-horizon discounted Markov decision processes
• Finite state approximation for denumerable-state infinite horizon contracted Markov decision processes: The policy space method
• The blast furnaces problem
• Contraction mappings underlying undiscounted Markov decision problems
• Dynamic programming, Markov chains, and the method of successive approximations
• Finite-state approximations to denumerable-state dynamic programs
• Iterative solution of the functional equations of undiscounted Markov renewal programming
• The asymptotic behaviour of the minimal total expected cost for the denumerable state Markov decision model
• The optimality equation in average cost denumerable state semi-Markov decision problems, recurrency conditions and algorithms
• An approximation procedure for stochastic dynamic programming in countable state space
• Approximations of Dynamic Programs, I
• A note on simultaneous recurrence conditions on a set of denumerable stochastic matrices
• Approximation procedure for stochastic dynamic programming based on clustering of state and action spaces
• Approximations of Dynamic Programs, II
• Contraction Mappings in the Theory Underlying Dynamic Programming

This page was built for publication: Finite state approximation algorithms for average cost denumerable state Markov decision processes