Title: MIRACLE: Model-free Imitation and Reinforcement Learning for Adaptive Cut-Selection
Primary area: reinforcement learning
Keywords: Model-Based Reinforcement Learning, Adversarial Reward Learning, Proximal Policy Optimization, Mixed-Integer Programming, Combinatorial Optimization

Abstract: Mixed-Integer Programming (MIP) solvers rely heavily on cutting planes to tighten LP relaxations, but traditional approaches generate thousands of cuts that consume gigabytes of memory while providing minimal benefit. We present an intelligent cut selection framework that achieves a  98.1\% reduction in memory usage while maintaining competitive solving with around 0.08\% objective gap. Within this RL framework, we use Proximal Policy Optimization (PPO) to learn a behavioral model that imitates the expert solver’s decisions. The adversarially imitated behavioral model drives an agent comprising these key innovations: (i) a cut-selection policy trained via curriculum learning; and (ii) adaptive inference that dynamically adjusts computational budgets. Through comprehensive evaluation across SetCover and diverse MIPLIB problems, we demonstrate consistent speedups (3.78$\times$ average on MIPLIB) and achieve a 100\% success rate on instances where traditional SCIP fails 47-53\% of the time. Our method also reduces peak memory consumption from 3.03GB to 46MB, enabling optimization in previously inaccessible environments and other resource-constrained environments where traditional solvers face fundamental limitations.

TL;DR: We propose a framework that reduces memory consumption of Mixed-Integer Programming solver using Reinforcement Learning and Imitation Learning