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Stability and Admissibility Analysis for Zero-Sum Games Under General Value Iteration Formulation

Ding Wang, Mingming Zhao, Mingming Ha, Junfei Qiao

2022IEEE Transactions on Neural Networks and Learning Systems56 citationsDOI

Abstract

In this article, the general value iteration (GVI) algorithm for discrete-time zero-sum games is investigated. The theoretical analysis focuses on stability properties of the systems and also the admissibility properties of the iterative policy pair. A new criterion is established to determine the admissibility of the current policy pair. Besides, based on the admissibility criterion, the improved GVI algorithm toward zero-sum games is developed to guarantee that all iterative policy pairs are admissible if the current policy pair satisfies the criterion. On the basis of the attraction domain, we demonstrate that the state trajectory will stay in the region using the fixed or the evolving policy pair if the initial state belongs to the domain. It is emphasized that the evolving policy pair can stabilize the controlled system. These theoretical results are applied to linear and nonlinear systems via offline and online critic control design.

Topics & Concepts

Stability (learning theory)TrajectoryMathematicsState (computer science)Basis (linear algebra)Value (mathematics)Nonlinear systemIterative methodMathematical optimizationCurrent (fluid)Control (management)Linear systemApplied mathematicsMathematical economicsComputer scienceControl theory (sociology)Optimal controlInitial value problemFixed pointControl systemAdaptive Dynamic Programming ControlReinforcement Learning in RoboticsAdvanced Control Systems Optimization