Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications
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.
Cite
@article{arxiv.2101.07156,
title = {Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications},
author = {Max Cohen and Calin Belta},
journal= {arXiv preprint arXiv:2101.07156},
year = {2021}
}
Comments
To appear at the 24th ACM International Conference on Hybrid Systems: Computation and Control