Convergence Proof of Approximate Policy Iteration for Undiscounted Optimal Control of Discrete-Time Systems-Reference-Cited by-同舟云学术

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Convergence Proof of Approximate Policy Iteration for Undiscounted Optimal Control of Discrete-Time Systems

Published:2015-08-25 Issue:6 Volume:7 Page:763-771
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Zhu Yuanheng,Zhao Dongbin,He Haibo,Ji Junhong

Funder

National Natural Science Foundation of China

State Key Laboratory of Robotics and System

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

http://link.springer.com/content/pdf/10.1007/s12559-015-9350-z.pdf

Reference34 articles.

1. Abu-Khalaf M, Lewis FL. Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach. Automatica. 2005;41(5):779–91.

2. Abu-Khalaf M, Lewis F, Huang J. Policy iterations on the Hamilton–Jacobi–Isaacs equation for $$\text{ H }_{\infty }$$ H ∞ state feedback control with input saturation. IEEE Trans Autom Control. 2006;51(12):1989–95.

3. Al-Tamimi A, Abu-Khalaf M, Lewis F. Adaptive critic designs for discrete-time zero-sum games with application to $$\text{ H }_{\infty }$$ H ∞ control. IEEE Trans Syst Man Cybern B. 2007;37(1):240–7.

4. Al-Tamimi A, Lewis F, Abu-Khalaf M. Discrete-time nonlinear HJB solution using approximate dynamic programming: Convergence proof. IEEE Trans Syst Man Cybern B. 2008;38(4):943–9.

5. Barty K, Girardeau P, Roy JS, Strugarek C. Q-learning with continuous state spaces and finite decision set. In: Proceedings of the 2007 IEEE international symposium on approximate dynamic programming and reinforcement learning (ADPRL 2007); 2007. pp. 346–351.

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