Jaime Brugueras : Finding Optimal Policies in Markov Decision Processes and Stochastic Games

Posted by , part of the Statistics and Data Science Seminar.

At

Oct. 12, 2005, 3 p.m.

In

SEO 512

Abstract

Markov decision processes (MDP) are stochastic processes that describe the evolution of dynamic systems controlled by sequences of decisions or actions. Different paths of the system lead to associated economic consequences; the ultimate aim is to take those actions that optimize a certain criterion. I will review the mathematical model of such processes, give real-life examples, and describe the well-known algorithms for finding optimal policies. Stochastic games are a natural generalization of MDP to the case of two or more controllers. Existence of finite algorithms for finding optimal stationary policies is in general an open problem. I consider a special class of stochastic games, those with perfect information, which can be solved via a finite algorithm.

Drinks, Cake, ......