非亲系系统的基于自适应动态编程的等级决策
Danyu Lin1, Shan Xue2, Derong Liu3
1School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
本研究介绍了一种自适应动态编程方法,用于解决非亲系系统中复杂的多人分层决策问题. 该方法将非关联系统转化为关联形式,为所有参与者提供最佳的控制策略.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 系统工程 系统工程
背景情况:
- 多玩家层次决策对非关联系统构成挑战.
- 现有的方法可能需要复杂的系统转换或初始控制规律.
研究的目的:
- 开发一种自适应动态编程算法,用于解决非亲系系统中的多人层次决策问题.
- 为了简化控制,将非亲系系统转换为同等亲系形式.
- 将这个问题建模成一个用于战略决策的斯塔克尔伯格游戏.
主要方法:
- 使用动态反和原始系统动态构建一个亲属增强系统.
- 把问题建模成一个斯塔克尔伯格游戏,有领导者和追随者.
- 使用单个关键神经网络 (NN) 来近似值函数并推导出最佳控制策略.
- 采用利亚普诺夫理论来确保系统稳定性和NN重量趋同.
- 在权重更新法中引入一个额外的术语,以消除对初始可接受控制法的需要.
主要成果:
- 成功地将非相亲的多人游戏系统转化为一般的相亲形式.
- 在Stackelberg游戏框架内为每个玩家开发了最佳的控制策略.
- 使用利亚普诺夫理论证明了系统状态的统一终极边界性和NN重量误差.
- 通过模拟验证了算法的可行性和有效性.
结论:
- 拟议的自适应动态编程方法有效地解决了非亲系系统的多人分层决策.
- 转换为亲属增强系统简化了控制问题.
- 使用单一的批评者NN和修改的权重更新法提高了算法的实用性.
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