Solving generalized semi-Markov decision processes using continuous phase-type distributions

Abstract

We introduce the generalized semi-Markov decision process (GSMDP) as an extension of continuous-time MDPs and semi-Markov decision processes (SMDPs) for modeling stochastic decision processes with asynchronous events and actions. Using phase-type distributions and uniformization, we show how an arbitrary GSMDP can be approximated by a discrete-time MDP, which can then be solved using existing MDP techniques. The techniques we present can also be seen as an alternative approach for solving SMDPs, and we demonstrate that the introduction of phases allows us to generate higher quality policies than those obtained by standard SMDP solution techniques.

Sample citation

Håkan L. S. Younes and Reid G. Simmons. 2004. Solving generalized semi-Markov decision processes using continuous phase-type distributions. In Proceedings of the Nineteenth National Conference on Artificial Intelligence, 742–747, San Jose, California. AAAI Press.