Variations on the Stochastic Shortest Path Problem

TL;DR

This paper revisits the stochastic shortest path problem, introducing algorithms that provide multiple guarantees on path length distributions, advancing beyond traditional expected value minimization.

Contribution

It presents new algorithms for strategy synthesis that offer distributional guarantees, building on recent advances in Markov decision process theory.

Findings

Algorithms for distributional guarantees on path lengths

Improved strategies over classical solutions

Application of recent MDP results

Abstract

In this invited contribution, we revisit the stochastic shortest path problem, and show how recent results allow one to improve over the classical solutions: we present algorithms to synthesize strategies with multiple guarantees on the distribution of the length of paths reaching a given target, rather than simply minimizing its expected value. The concepts and algorithms that we propose here are applications of more general results that have been obtained recently for Markov decision processes and that are described in a series of recent papers.