Statistics and Data Science Seminar
Jaime Brugueras
UIC: Statistics, MSCS
Finding Optimal Policies in Markov Decision Processes and Stochastic Games
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, ......
Wednesday October 12, 2005 at 3:00 PM in SEO 512