AI RESEARCH
Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization
arXiv CS.LG
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ArXi:2511.09219v4 Announce Type: replace Mixed-Integer Linear Programming (MILP) lies at the core of many real-world combinatorial optimization (CO) problems, traditionally solved by branch-and-bound (B&B). A key driver influencing B&B solvers efficiency is the variable selection heuristic that guides branching decisions. Looking to move beyond static, hand-crafted heuristics, recent work has explored adapting traditional reinforcement learning (RL) algorithms to the B&B setting, aiming to learn branching strategies tailored to specific MILP distributions.