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Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization

arXiv — cs.LG•Thursday, November 13, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The introduction of Plan-and-Branch-and-Bound (PlanB&B) marks a significant advancement in the field of combinatorial optimization, particularly for Mixed-Integer Linear Programming (MILP). Traditional branch-and-bound (B&B) methods rely heavily on variable selection heuristics, which can limit their efficiency. By integrating model-based reinforcement learning (MBRL), PlanB&B aims to learn and adapt branching strategies tailored to specific MILP distributions. This innovative approach builds on the success of reinforcement learning agents in board games, which have demonstrated remarkable capabilities in similar combinatorial problem-solving contexts. Through computational experiments, PlanB&B has shown to outperform previous state-of-the-art reinforcement learning methods across four standard MILP benchmarks, validating its effectiveness. This development not only enhances the efficiency of B&B solvers but also opens new avenues for tackling complex optimization challenges in various…
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