Title

A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation

Authors

Tom Erez, Washington University in St LouisFollow
William D. Smart, Washington University in St Louis

Document Type

Technical Report

Department

Computer Science and Engineering

Publication Date

2010

Filename

WUCSE-2010-28.pdf

DOI:

10.7936/K7MW2FBD

Technical Report Number

WUCSE-2010-28

Abstract

Partially-Observable Markov Decision Processes (POMDPs) are typically solved by finding an approximate global solution to a corresponding belief-MDP. In this paper, we offer a new planning algorithm for POMDPs with continuous state, action and observation spaces. Since such domains have an inherent notion of locality, we can find an approximate solution using local optimization methods. We parameterize the belief distribution as a Gaussian mixture, and use the Extended Kalman Filter (EKF) to approximate the belief update. Since the EKF is a first-order filter, we can marginalize over the observations analytically. By using feedback control and state estimation during policy execution, we recover a behavior that is effectively conditioned on incoming observations despite the unconditioned planning. Local optimization provides no guarantees of global optimality, but it allows us to tackle domains that are at least an order of magnitude larger than the current state-of-the-art. We demonstrate the scalability of our algorithm by considering a simulated hand-eye coordination domain with 16 continuous state dimensions and 6 continuous action dimensions.

Comments

Permanent URL: http://dx.doi.org/10.7936/K7MW2FBD

Recommended Citation

Erez, Tom and Smart, William D., " A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation" Report Number: WUCSE-2010-28 (2010). All Computer Science and Engineering Research.
https://openscholarship.wustl.edu/cse_research/42