Average Case Analysis of the Classical Algorithm for Markov Decision Processes with B\"uchi Objectives

TL;DR

This paper analyzes the average case complexity of the classical algorithm for computing almost-sure winning vertices in MDPs with B"uchi objectives, showing it is typically efficient with linear expected running time.

Contribution

It provides the first average case complexity analysis of the classical algorithm for B"uchi objectives in MDPs, demonstrating linear expected running time under various conditions.

Findings

Expected number of iterations is logarithmic for constant out-degree MDPs.

Expected number of iterations is constant for general MDPs.

Probability of requiring more than constant iterations is exponentially small.

Abstract

We consider Markov decision processes (MDPs) with $\omega$-regular specifications given as parity objectives. We consider the problem of computing the set of almost-sure winning vertices from where the objective can be ensured with probability 1. The algorithms for the computation of the almost-sure winning set for parity objectives iteratively use the solutions for the almost-sure winning set for B\"uchi objectives (a special case of parity objectives). We study for the first time the average case complexity of the classical algorithm for computing almost-sure winning vertices for MDPs with B\"uchi objectives. Our contributions are as follows: First, we show that for MDPs with constant out-degree the expected number of iterations is at most logarithmic and the average case running time is linear (as compared to the worst case linear number of iterations and quadratic time complexity). Second, we show that for general MDPs the expected number of iterations is constant and the average case running time is linear (again as compared to the worst case linear number of iterations and quadratic time complexity). Finally we also show that given all graphs are equally likely, the probability that the classical algorithm requires more than constant number of iterations is exponentially small.