[3] YIN H, YANG Y F, ZHAO Y, Self-tuning controller design for a 2-DOF flight attitude simulator［J］. Electric Machines and Control, 2018,22(4): 105-112. (in Chinese)
[5] RECHT B . A tour of reinforcement learning: the view from continuous control［Z/OL］. https: //arxiv.org/pdf/1806.09460.pdf. ［2018-09-09］.
[6] DONG L, GUANG-HONG Y . Model-free adaptive control design for nonlinear discrete-time processes with reinforcement learning techniques［J］. International Journal of Systems Science, 2018, 49(11): 2298-2308.
[7] LEVINE S. Reinforcement learning and control as probabilistic inference: tutorial and review［Z/OL］. https: //arxiv.org/abs/1805.00909. ［2018-05-20］.
[8] ZHANG T. Research on Path Planning Method of Quadrotor UAV Based on Reinforcement Learning［D］. Harbin: Harbin Institute of Technology, 2018(in Chinese)
[9] KAELBLING L P, LITTMAN M L, MOORE A W. Reinforcement learning: a survey［J］. Artificial Intelligence Research, 1996, 4(1): 237-285.
[10] CHUA K , CALANDRA R , MCALLISTER R , et al.. Deep reinforcement learning in a handful of trials using probabilistic dynamics models［Z/OL］. https: //arxiv.org/abs/1805.12114. ［2018-11-02］.
[11] DEISENROTH M, RASMUSSEN C. PILCO: A model-based and data-efficient approach to policy search［C］. International Conference on International Conference on Machine Learning. Omnipress, 2011.
[12] RICHARD S, ANDREW G. Reinforcement Learning: An Introduction［M］. Second Edition. London: The MIT Press,2016: 78-88
[13] DURRANT-WHYTE H, ROY N, ABBEEL P. Learning to control a low-cost manipulator using data-efficient reinforcement learning［C］. Robotics: Science and Systems VII. MIT Press, 2011.
[14] DEISENROTH M P. Efficient Reinforcement Learning using Gaussian Processes［D］. Karlsruhe: Karlsruhe Institute of Technology, 2015.
