Finite state approximations for denumerable state infinite horizon discounted Markov decision processes with unbounded rewards

DOI10.1016/0022-247X(82)90271-2zbMath0533.90094MaRDI QIDQ790055

Douglas J. White

Publication date: 1982

Published in: Journal of Mathematical Analysis and Applications (Search for Journal in Brave)

zbMATH Keywords

approximation errorsMarkov decision problems262.90064309.90017354.90087428.90082431.90080denumerable statesunbounded reward vector

Mathematics Subject Classification ID

Markov and semi-Markov decision processes (90C40)

Related Items (8)

Approximating Markov decision processes using expected state transitions ⋮ Finite-state approximations for denumerable multidimensional state discounted Markov decision processes ⋮ Finite-state approximations for denumerable state discounted Markov decision processes ⋮ Asymptotic properties of constrained Markov Decision Processes ⋮ A priori bounds for approximations of Markov programs ⋮ Finite state approximation for denumerable-state infinite horizon contracted Markov decision processes: The policy space method ⋮ Finite state approximations for denumerable state infinite horizon discounted Markov decision processes with unbounded rewards ⋮ A stability result for linear Markovian stochastic optimization problems

Cites Work

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• Finite state approximations for denumerable state infinite horizon discounted Markov decision processes with unbounded rewards
• 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
• Markov programming by successive approximations with respect to weighted supremum norms
• On Dynamic Programming with Unbounded Rewards
• Note—A Note on Dynamic Programming with Unbounded Rewards
• Contraction Mappings in the Theory Underlying Dynamic Programming
• Discrete Dynamic Programming with Unbounded Rewards

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