Title: Neural-Network-Based Synchronous Iteration Learning Method for Multi-Player Zero-Sum Games
Authors: Song, RZ; Wei, QL; Song, BA
Author Full Names: Song, Ruizhuo; Wei, Qinglai; Song, Biao
Source: NEUROCOMPUTING, 242 73-82; 10.1016/j.neucom.2017.02.051 JUN 14 2017
Language: English
Abstract: In this paper, a synchronous solution method for multi-player zero-sum games without system dynamics is established based on neural network. The policy iteration (PI) algorithm is presented to solve the Hamilton-Jacobi-Bellman (HJB) equation. It is proven that the obtained iterative cost function is convergent to the optimal game value. For avoiding system dynamics, off-policy learning method is given to obtain the iterative cost function, controls and disturbances based on Pl. Critic neural network (CNN), action neural networks (ANNs) and disturbance neural networks (DNNs) are used to approximate the cost function, controls and disturbances. The weights of neural networks compose the synchronous weight matrix, and the uniformly ultimately bounded (UUB) of the synchronous weight matrix is proven. Two examples are given to show that the effectiveness of the proposed synchronous solution method for multi-player ZS games. (C) 2017 Elsevier B.V. All rights reserved.
ISSN: 0925-2312
eISSN: 1872-8286
IDS Number: ES9ES
Unique ID: WOS:000399859500007