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Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors
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DOI：10.1109/TNNLS.2017.2661865.png)
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Abstract
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In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.
Keywords:
Adaptive critic designs
adaptive dynamic programming (ADP)
approximate dynamic programming
generalized policy iteration (GPI)
neural networks
neurodynamic programming
nonlinear systems
optimal control
reinforcement learning
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