CORL：通过分支定界求解的混合整数线性规划策略强化学习
CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound
AI总结：
CORL通过强化学习端到端优化MILP策略，提升现实世界决策性能。
摘要：
组合序列决策问题通常被建模为混合整数线性规划（MILP）并通过分支定界（B&B）算法求解。MILP模型准确表示随机现实世界问题的固有难度导致现实中的性能欠佳。最近，机器学习方法被应用于构建MILP模型以提高决策质量，而非如何准确建模现实问题。然而，这些方法通常依赖监督学习，假设能够获得真正的最优决策，并使用替代品来近似MILP梯度。在本工作中，我们引入了一个概念验证的CORL框架，通过在真实世界数据上使用强化学习（RL）端到端微调MILP方案，以最大化其操作性能。我们通过将由B&B求解的MILP转换为可微的随机策略，使其兼容RL。我们在一个简单的示例中验证了CORL方法在组合序列决策中的有效性。
英文摘要：
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 decision making example.
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