Reinforcement learning based local search for grouping problems: A case study on graph coloring

TL;DR

This paper introduces a reinforcement learning-based local search method for grouping problems, specifically applied to graph coloring, demonstrating competitive performance on standard benchmarks.

Contribution

It presents a novel general approach combining reinforcement learning with local search for grouping problems, validated on graph coloring.

Findings

RLS achieves competitive results on DIMACS and COLOR02 benchmarks.

The approach effectively integrates reinforcement learning with descent-based local search.

Experimental results show promising performance compared to existing algorithms.

Abstract

Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search. The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied. Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms.