Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915910067945472 |
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| author | Strang, Paul Alès, Zacharie Bissuel, Côme Juan, Olivier Kedad-Sidhoum, Safia Rachelson, Emmanuel |
| author_facet | Strang, Paul Alès, Zacharie Bissuel, Côme Juan, Olivier Kedad-Sidhoum, Safia Rachelson, Emmanuel |
| contents | 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. In parallel, RL agents have achieved remarkable success in board games, a very specific type of combinatorial problems, by leveraging environment simulators to plan via Monte Carlo Tree Search (MCTS). Building on these developments, we introduce Plan-and-Branch-and-Bound (PlanB&B), a model-based reinforcement learning (MBRL) agent that leverages a learned internal model of the B&B dynamics to discover improved branching strategies. Computational experiments empirically validate our approach, with our MBRL branching agent outperforming previous state-of-the-art RL methods across four standard MILP benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09219 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization Strang, Paul Alès, Zacharie Bissuel, Côme Juan, Olivier Kedad-Sidhoum, Safia Rachelson, Emmanuel Machine Learning 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. In parallel, RL agents have achieved remarkable success in board games, a very specific type of combinatorial problems, by leveraging environment simulators to plan via Monte Carlo Tree Search (MCTS). Building on these developments, we introduce Plan-and-Branch-and-Bound (PlanB&B), a model-based reinforcement learning (MBRL) agent that leverages a learned internal model of the B&B dynamics to discover improved branching strategies. Computational experiments empirically validate our approach, with our MBRL branching agent outperforming previous state-of-the-art RL methods across four standard MILP benchmarks. |
| title | Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.09219 |