Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties.

Autor:	Mahmud SMN; School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK, United States., Nivison SA; Munitions Directorate, Air Force Research Laboratory, Eglin AFB, FL, United States., Bell ZI; Munitions Directorate, Air Force Research Laboratory, Eglin AFB, FL, United States., Kamalapurkar R; School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in robotics and AI [Front Robot AI] 2021 Dec 16; Vol. 8, pp. 733104. Date of Electronic Publication: 2021 Dec 16 (Print Publication: 2021).
DOI:	10.3389/frobt.2021.733104
Abstrakt:	Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases. In general, safety guarantees are critical in reinforcement learning when the system is safety-critical and/or task restarts are not practically feasible. In optimal control theory, safety requirements are often expressed in terms of state and/or control constraints. In recent years, reinforcement learning approaches that rely on persistent excitation have been combined with a barrier transformation to learn the optimal control policies under state constraints. To soften the excitation requirements, model-based reinforcement learning methods that rely on exact model knowledge have also been integrated with the barrier transformation framework. The objective of this paper is to develop safe reinforcement learning method for deterministic nonlinear systems, with parametric uncertainties in the model, to learn approximate constrained optimal policies without relying on stringent excitation conditions. To that end, a model-based reinforcement learning technique that utilizes a novel filtered concurrent learning method, along with a barrier transformation, is developed in this paper to realize simultaneous learning of unknown model parameters and approximate optimal state-constrained control policies for safety-critical systems. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Mahmud, Nivison, Bell and Kamalapurkar.)
Databáze:	MEDLINE
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