Abstract

The Vertex Coloring Problem (VCP) is a fundamental NP-hard problem with applications in wireless networks, compiler design, scheduling, etc. We present VColRL, a deep reinforcement learning (DRL) framework that learns to color graphs quickly by leveraging a reduction-based approach that progressively reduces the graph at each step. The core novelty of VColRL is a new Markov Decision Process (MDP) formulation tailored for VCP that assigns colors to multiple vertices at each step, incorporates a rollback mechanism to revert all conflicting vertices to the undecided state, and employs a reward function designed to minimize the highest-indexed color used. Experiments on synthetic and benchmark graphs show that VColRL improves color usage over optimization solvers and prior learning-based methods, remains competitive with search-based heuristics and metaheuristics, and achieves fast runtime, while generalizing well to diverse graph families despite being trained only on synthetic graphs from a single family.