Variational characterizations in Markov decision processes

Related Items (2)

Bounds on the fixed point of a monotone contraction operator ⋮ Contraction mappings underlying undiscounted Markov decision problems

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
Unnamed Item
On the solvability of Bellman's functional equations for Markov renewal programming
A modified dynamic programming method for Markovian decision problems
Iterative solution of the functional equations of undiscounted Markov renewal programming
On the solvability of Bellman's functional equation for a Markovian decision process
Successive Approximation Methods for Solving Nested Functional Equations in Markov Decision Problems
Linear Programming and Markov Decision Chains
A UNIFIED APPROACH TO ALGORITHMS WITH A SUBOPTIMALITY TEST IN DISCOUNTED SEMI-MARKOV DECISION PROCESSES
Action Elimination Procedures for Modified Policy Iteration Algorithms
Note—A Test for Nonoptimal Actions in Undiscounted Finite Markov Decision Chains
The Functional Equations of Undiscounted Markov Renewal Programming
Geometric convergence of value-iteration in multichain Markov decision problems
Markov-Renewal Programming. I: Formulation, Finite Return Models
Contraction Mappings in the Theory Underlying Dynamic Programming
Letter to the Editor—A Test for Suboptimal Actions in Markovian Decision Problems
Discrete Dynamic Programming with a Small Interest Rate
Discrete Dynamic Programming with Sensitive Discount Optimality Criteria
On Finding the Maximal Gain for Markov Decision Processes
Perturbation theory and finite Markov chains
Multichain Markov Renewal Programs
Technical Note—Bounds on the Gain of a Markov Decision Process
Some Bounds for Discounted Sequential Decision Processes
Markov Renewal Programs with Small Interest Rates
Tests for Suboptimal Actions in Discounted Markov Programming
Technical Note—Elimination of Suboptimal Actions in Markov Decision Problems
Potentials for denumerable Markov chains

This page was built for publication: Variational characterizations in Markov decision processes