Finding Approximate POMDP Solutions Through Belief Compression

Nicholas Roy, nickroy@mit.edu
Massachusetts Institute of Technology,
Computer Science and Artificial Intelligence Laboratory
Cambridge, MA

Geoffrey Gordon, ggordon@cs.cmu.edu
Carnegie Mellon University, School of Computer Science
Pittsburgh, PA

Sebastian Thrun, thrun@stanford.edu
Stanford University, Computer Science Department
Stanford, CA

Abstract:

Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are generally considered to be intractable for large models. The intractability of these algorithms is to a large extent a consequence of computing an exact, optimal policy over the entire belief space. However, in real-world POMDP problems, computing the optimal policy for the full belief space is often unnecessary for good control even for problems with complicated policy classes. The beliefs experienced by the controller often lie near a structured, low-dimensional subspace embedded in the high-dimensional belief space. Finding a good approximation to the optimal value function for only this subspace can be much easier than computing the full value function.

We introduce a new method for solving large-scale POMDPs by reducing the dimensionality of the belief space. We use Exponential family Principal Components Analysis [CDS02] to represent sparse, high-dimensional belief spaces using small sets of learned features of the belief state. We then plan only in terms of the low-dimensional belief features. By planning in this low-dimensional space, we can find policies for POMDP models that are orders of magnitude larger than models that can be handled by conventional techniques.

We demonstrate the use of this algorithm on a synthetic problem and on mobile robot navigation tasks.