|
| 论文编号: |
|
| 第一作者所在部门: |
|
| 中文论文题目: |
Neural-network-based synchronous iteration learning method for multi-player zero-sum games
|
| 英文论文题目: |
Neural-network-based synchronous iteration learning method for multi-player zero-sum games
|
| 论文题目英文: |
|
| 作者: |
Song, Ruizhuo
|
| 论文出处: |
|
| 刊物名称: |
NEUROCOMPUTING
|
| 年: |
2017
|
| 卷: |
242 |
| 期: |
|
| 页: |
73-82 |
| 联系作者: |
|
| 收录类别: |
|
| 影响因子: |
|
| 摘要: |
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. |
| 英文摘要: |
|
| 外单位作者单位: |
|
| 备注: |
|
关闭窗口 |
|
