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arXiv · 1604.00377

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

Yangming Zhou·Jin-Kao Hao·Béatrice Duval
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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.

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Yangming Zhou, Jin-Kao Hao, Béatrice Duval. 2016-04-01. Reinforcement learning based local search for grouping problems: A case study on graph coloring. https://arxiv.org/abs/1604.00377

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