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Building on the receding horizon approach by Hernandez-Lerma andLasserre in solving Markov decision processes (MDPs),this paper first analyzes the performance of the (approximate) receding horizon approach in terms of ...

In this paper, we discuss implementation issues of applying a simulation-based approach to asemiconductor fab-level decision making problem. The fab-level decision making problem isformulated as a Markov Decision Process ...

Stability issues in a connectionless, one-hop queueing system featuringservers with overlapping service regions (e.g. a Mode Select (Mode S) Radarcommunications network or part of an Aeronautical Telecommunications Network ...

We proposetwo finite difference two-timescale simultaneous perturbationstochastic approximation (SPSA)algorithmsfor simulation optimization ofhidden Markov models. Stability and convergence of both thealgorithms is ...

We present a novel algorithm, called ``Simulated Annealing Multiplicative Weights", for approximately solving large finite-horizon stochastic dynamic programming problems. The algorithm is ``asymptotically efficient" in ...

Research on numerical solution methods for partially observable Markov decision processes (POMDPs) has primarily focused on discrete-state models, and these algorithms do not generally extend to continuous-state POMDPs, ...

This paper proposes a simple analytical model called M time-scale MarkovDecision Process (MMDP) for hierarchically structured sequential decision making processes, where decisions in each level in the M-level hierarchy are ...

We consider a receding horizon approach as an approximate solution totwo-person zero-sum Markov games with infinite horizon discounted costand average cost criteria. <p>We first present error bounds from the optimalequilibrium ...

We consider a rooted tree R to be a rooted subtree of a given tree T if the tree obtained by replacing the directed arcs of R by undirected edges is a subtree of T. In this work, we study the problem of assigning colors ...