论文标题
基于抽象的分支和结合方法Q学习用于混合最佳控制
Abstraction-based branch and bound approach to Q-learning for hybrid optimal control
论文作者
论文摘要
在本文中，我们设计了一个理论框架，允许对混合系统应用模型预测控制。为此，我们通过利用交替模拟的概念来开发近似动态编程的理论。我们展示了如何将这些概念组合到分支和结合算法中，这些算法可以使用拉格朗日二元性进一步完善Q-功能。我们在数字示例上说明了方法。
In this paper, we design a theoretical framework allowing to apply model predictive control on hybrid systems. For this, we develop a theory of approximate dynamic programming by leveraging the concept of alternating simulation. We show how to combine these notions in a branch and bound algorithm that can further refine the Q-functions using Lagrangian duality. We illustrate the approach on a numerical example.
