2021
L4DC
L4DC 2021
Abstraction-based branch and bound approach to Q-learning for hybrid optimal control
Abstract
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.
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization and Reinforcement Learning and Robotics
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Keyword Pioneer
— hybrid optimal control
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio