CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound

📝 Original Info

• Title: CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
• ArXiv ID: 2512.11169
• Date: 2025-12-11
• Authors: Akhil S Anand, Elias Aarekol, Martin Mziray Dalseg, Magnus Stalhane, Sebastien Gros

📝 Abstract

Combinatorial sequential decision-making problems are typically modeled as mixed-integer linear programs (MILPs) and solved via branch-and-bound (B&B) algorithms. The inherent difficulty of modeling MILPs that accurately represent stochastic real-world problems leads to suboptimal performance in the real world. Recently, machine-learning methods have been applied to build MILP models for decision quality rather than how accurately they model the real-world problem. However, these approaches typically rely on supervised learning, assume access to true optimal decisions, and use surrogates for the MILP gradients. In this work, we introduce a proof-of-concept CORL framework that end-to-end fine-tunes an MILP scheme using reinforcement learning (RL) on real-world data to maximize its operational performance. We enable this by casting an MILP solved by B&B as a differentiable stochastic policy compatible with RL. We validate the CORL method in a simple illustrative combinatorial sequential decisionmaking example.

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