Galerkin approximations of the generalized Hamilton-Jacobi-Bellman equation
Automatica (Journal of IFAC)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
Brief paper: A neural network solution for fixed-final time optimal control of nonlinear systems
Automatica (Journal of IFAC)
Brief paper: Adaptive optimal control for continuous-time linear systems based on policy iteration
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Reinforcement learning and adaptive dynamic programming for feedback control
IEEE Circuits and Systems Magazine
Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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
Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatica (Journal of IFAC)
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
IEEE Transactions on Neural Networks
Approximate Dynamic Programming for Optimal Stationary Control With Control-Dependent Noise
IEEE Transactions on Neural Networks - Part 2
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In this paper, we solve the zero-sum game problems for discrete-time affine nonlinear systems with known dynamics via iterative adaptive dynamic programming algorithm. First, a greedy heuristic dynamic programming iteration algorithm is developed to solve the zero-sum game problems, which can be used to solve the Hamilton-Jacobi-Isaacs equation associated with H"~ optimal regulation control problems. The convergence analysis in terms of value function and control policy is provided. To facilitate the implementation of the algorithm, three neural networks are used to approximate the control policy, the disturbance policy, and the value function, respectively. Then, we extend the algorithm to H"~ optimal tracking control problems through system transformation. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed scheme.