Correlated Action Effects in Decision Theoretic Regression

TL;DR

This paper introduces a new decision theoretic regression operator that effectively handles actions with correlated effects in Markov decision processes, improving the tractability of structured policy construction.

Contribution

It presents a novel regression operator that extends existing methods to manage correlated action effects in decision theoretic planning.

Findings

The new operator correctly models correlated effects in actions.

It enhances the applicability of structured policy construction methods.

The approach maintains computational feasibility despite increased complexity.

Abstract

Much recent research in decision theoretic planning has adopted Markov decision processes (MDPs) as the model of choice, and has attempted to make their solution more tractable by exploiting problem structure. One particular algorithm, structured policy construction achieves this by means of a decision theoretic analog of goal regression using action descriptions based on Bayesian networks with tree-structured conditional probability tables. The algorithm as presented is not able to deal with actions with correlated effects. We describe a new decision theoretic regression operator that corrects this weakness. While conceptually straightforward, this extension requires a somewhat more complicated technical approach.