Perrusquía, Adolfo2022-03-232022-03-232022-03-05Perrusquia A. (2022) Solution of the linear quadratic regulator problem of black box linear systems using reinforcement learning, Information Sciences, Volume 595, May 2022, pp. 364-3770020-0255https://doi.org/10.1016/j.ins.2022.03.004https://dspace.lib.cranfield.ac.uk/handle/1826/17670In this paper, a Q-learning algorithm is proposed to solve the linear quadratic regulator problem of black box linear systems. The algorithm only has access to input and output measurements. A Luenberger observer parametrization is constructed using the control input and a new output obtained from a factorization of the utility function. An integral reinforcement learning approach is used to develop the Q-learning approximator structure. A gradient descent update rule is used to estimate on-line the parameters of the Q-function. Stability and convergence of the Q-learning algorithm under the Luenberger observer parametrization is assessed using Lyapunov stability theory. Simulation studies are carried out to verify the proposed approach.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Linear quadratic regulatorState observer parametrizationQ-learningGradient descentOutput feedbackPersistency of excitationArticle1872-6291