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arXiv · 2101.07156

Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications

Max Cohen·Calin Belta
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Abstract

In this paper we study the problem of synthesizing optimal control policies for uncertain continuous-time nonlinear systems from syntactically co-safe linear temporal logic (scLTL) formulas. We formulate this problem as a sequence of reach-avoid optimal control sub-problems. We show that the resulting hybrid optimal control policy guarantees the satisfaction of a given scLTL formula by constructing a barrier certificate. Since solving each optimal control problem may be computationally intractable, we take a learning-based approach to approximately solve this sequence of optimal control problems online without requiring full knowledge of the system dynamics. Using Lyapunov-based tools, we develop sufficient conditions under which our approximate solution maintains correctness. Finally, we demonstrate the efficacy of the developed method with a numerical example.

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BibTeXRIS

Max Cohen, Calin Belta. 2021-01-18. Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications. https://arxiv.org/abs/2101.07156

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