INTERLEAVE: A Faster Symbolic Algorithm for Maximal End Component Decomposition

TL;DR

The paper introduces INTERLEAVE, a symbolic algorithm that interleaves SCC computation with elimination of redundant pairs, leading to faster MEC decomposition in MDPs, solving more benchmarks and achieving significant speedups.

Contribution

INTERLEAVE is a novel symbolic algorithm that improves MEC decomposition efficiency by interleaving SCC computation with eager elimination, outperforming previous methods.

Findings

Solves 19 more benchmarks than previous algorithms.

Achieves an average of 3.81x speedup on solvable benchmarks.

Maintains the same theoretical complexity as prior approaches.

Abstract

This paper presents a novel symbolic algorithm for the Maximal End Component (MEC) decomposition of a Markov Decision Process (MDP). The key idea behind our algorithm INTERLEAVE is to interleave the computation of Strongly Connected Components (SCCs) with eager elimination of redundant state-action pairs, rather than performing these computations sequentially as done by existing state-of-the-art algorithms. Even though our approach has the same complexity as prior works, an empirical evaluation of INTERLEAVE on the standardized Quantitative Verification Benchmark Set demonstrates that it solves 19 more benchmarks (out of 379) than the closest previous algorithm. On the 149 benchmarks that prior approaches can solve, we demonstrate a 3.81x average speedup in runtime.