The interplay between Machine Learning (ML) and Constrained Optimization (CO) has recently been the subject of increasing interest, leading to a new and prolific research area covering (e.g.) Decision Focused Learning and Constrained Reinforcement Learning. Such approaches strive to tackle complex decision problems under uncertainty over multiple stages, involving both explicit (cost function, constraints) and implicit knowledge (from data), and possibly subject to execution time restrictions. While a good degree of success has been achieved, the existing methods still have limitations in terms of both applicability and effectiveness. For problems in this class, we propose UNIFY, a unified framework to design a solution policy for complex decision-making problems. Our approach relies on a clever decomposition of the policy in two stages, namely an unconstrained ML model and a CO problem, to take advantage of the strength of each approach while compensating for its weaknesses. With a little design effort, UNIFY can generalize several existing approaches, thus extending their applicability. We demonstrate the method effectiveness on two practical problems, namely an Energy Management System and the Set Multi-cover with stochastic coverage requirements. Finally, we highlight some current challenges of our method and future research directions that can benefit from the cross-fertilization of the two fields.
翻译:最近,机器学习(ML)与控制最佳化(CO)之间的相互作用日益引起人们的兴趣,导致一个新的和大量研究领域,包括(例如)决定重点学习和约束强化学习;这些方法力求在多个阶段解决不确定的复杂决策问题,涉及明确(成本功能、限制)和隐性知识(来自数据),并可能受到执行时间的限制。虽然取得了很大程度的成功,但现有方法在适用性和有效性方面仍然有局限性。关于这一类的问题,我们提议UNIFY为设计一个统一的框架,为复杂的决策问题设计一种解决办法政策。我们的方法依靠在两个阶段对政策进行精明的分解,即无限制的ML模式和CO问题,以便利用每种方法的力量,同时弥补其弱点。如果设计不费力,UNIFY可以概括现有的几种方法,从而扩大其适用性。我们展示了在能源管理系统和Set多功能化的多功能化政策方面的方法有效性,并提出了对未来两个领域有益的要求。最后,我们强调目前的方法和未来的研究领域的一些方向。