Speaker: Ronald Parr
Stanford University
When/Where: Tuesday October 12, 11am-12pm.
Doherty Hall 3313 (access by Wean Hall 8th floor)
Title: Density Estimation and Markov Decision Processes
Abstract:
The Markov Decision Process (MDP) and Partially Observable Markov
Decision Process (POMDP) frameworks are rich formal frameworks for
describing stochastic planning and control problems. In these problems
one is interested in finding a policy for acting that minimizes cost or
maximizes benefit. This is typically done either by constructing a
value function, which estimates the value of each state in the
environment, or by using some scheme for searching the space of
policies. Both of these methods have limitations when applied to very
large problems, making an efficient and fully general approach to
solving such problems an elusive goal.
In this talk I will give an overview of some recent work with colleagues
at Stanford and Berkeley on scaling (PO)MDP methods to solve larger
problems. The use of approximate probability distributions plays an
important role in each of these methods. In some cases it plays a
supporting role, while in others it offers a fairly radical departure
from traditional methods. I will present some basic theoretical results
and some promising preliminary simulation results showing the efficacy
of these methods. These results suggest that density estimation may
become as important as value function approximation in the search for
general and powerful methods for (PO)MDPs.
This talk will describe joint work with Daphne Koller, Andrew Ng, and
Andres Rodriguez.