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

This paper introduces CORL, a method for reinforcement learning of policies that solve Mixed-Integer Linear Programs (MILPs) using branch and bound algorithms. It addresses the challenges of suboptimal performance in real-world stochastic problems by improving MILP modeling, with significant potential for optimization and decision-making applications.