IEEE Transactions on Neural Networks
Generalized policy iteration for continuous-time systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Direct heuristic dynamic programming for nonlinear tracking control with filtered tracking error
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive dynamic programming: an introduction
IEEE Computational Intelligence Magazine
Finite horizon optimal tracking control for a class of discrete-time nonlinear systems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Optimal tracking control scheme for discrete-time nonlinear systems with approximation errors
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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In this correspondence, adaptive critic approximate dynamic programming designs are derived to solve the discrete-time zero-sum game in which the state and action spaces are continuous. This results in a forward-in-time reinforcement learning algorithm that converges to the Nash equilibrium of the corresponding zero-sum game. The results in this correspondence can be thought of as a way to solve the Riccati equation of the well-known discrete-time Hinfin optimal control problem forward in time. Two schemes are presented, namely: 1) a heuristic dynamic programming and 2) a dual-heuristic dynamic programming, to solve for the value function and the costate of the game, respectively. An Hinfin autopilot design for an F-16 aircraft is presented to illustrate the results