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Grouping problems aim to partition a set of items into multiple mutuallydisjoint subsets according to some specific criterion and constraints. Groupingproblems cover a large class of important combinatorial optimization problemsthat are generally computationally difficult. In this paper, we propose ageneral solution approach for grouping problems, i.e., reinforcement learningbased local search (RLS), which combines reinforcement learning techniques withdescent-based local search. The viability of the proposed approach is verifiedon a well-known representative grouping problem (graph coloring) where a verysimple descent-based coloring algorithm is applied. Experimental studies onpopular DIMACS and COLOR02 benchmark graphs indicate that RLS achievescompetitive performances compared to a number of well-known coloringalgorithms.

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