Edwin K. P. Chong, Scott A. Miller, Jason Adaska
Published 2011-11-14Version 1
We consider an example by Haviv (1996) of a constrained Markov decision process that, in some sense, violates Bellman's principle. We resolve this issue by showing how to preserve a form of Bellman's principle that accounts for a change of constraint at states that are reachable from the initial state.
A Primal-Dual Approach to Constrained Markov Decision Processes
Algorithm for Constrained Markov Decision Process with Linear Convergence
Subgradient methods with variants of Polyak stpsize for quasi-convex optimization with inequality constraints for analogues of sharp minima
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