A menu of designs for reinforcement learning over time
Neural networks for control
On-Line Learning Control for Discrete Nonlinear Systems Via an Improved ADDHP Method
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Adaptive Critic Designs for Discrete-Time Zero-Sum Games With Application to Control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatica (Journal of IFAC)
A self-learning call admission control scheme for CDMA cellular networks
IEEE Transactions on Neural Networks
Neurodynamic Programming and Zero-Sum Games for Constrained Control Systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In this paper, an adaptive dynamic programming (ADP)-based self-learning algorithm is developed for solving the two-person zero-sum differential games for continuous-time nonlinear systems with saturating controllers. Optimal control pair is iteratively obtained by the proposed ADP algorithm that makes the performance index function reach the saddle point of the zero-sum differential games. It shows that the iterative control pairs stabilize the nonlinear systems and the iterative performance index functions converge to the saddle point. Finally, a simulation example is given to illustrate the performance of the proposed method.