基于完全分布的神经网络的单调游戏方法,具有有限时间干扰拒绝
IEEE transactions on cybernetics
|September 24, 2025
概括
这项研究引入了一个分布式神经网络,用于在具有动态玩家的复杂游戏中找到变化的通用纳什平衡. 它增强了稳定性,并消除了参数预设计,以提高多代理系统的性能.
科学领域:
- 控制理论 控制理论
- 游戏理论 游戏理论
- 人工智能的人工智能
背景情况:
- 寻找变量通用纳什平衡 (vGNE) 对具有合约束的多代理系统至关重要.
- 现有的方法通常需要参数预设计,并且缺乏对干扰的稳定性.
- 动态玩家在一般单调游戏中实现平衡时引入了复杂性.
研究的目的:
- 开发一个分布式神经网络,以寻找vGNE在一般单调的游戏中,具有多个合约束.
- 为应对高阶动态和参数预设计要求所带来的挑战.
- 增强神经网络的稳定性和充分分布,以抵御外部干扰.
主要方法:
- 设计了一个分布式vGNE寻找神经网络 (vGSNN),使用高通波器将高阶动态转换为二阶动态.
- 提出了一个自适应式重量控制器,以消除对固定的参数预设计的需求,从而实现完整的分发.
- 包含了一个滑动模式控制器,用于有限时间的干扰排斥,并保持完整的分布.
主要成果:
- 拟议的vGSNN成功地将高阶动态转化为可管理的二阶动态.
- 适应性权重消除了对参数预设计的需求,实现了网络的完整分布.
- 滑动模式控制器确保了有限时间的干扰排斥,提高了强度.
- 通过无人机 (UAV) 群游戏模拟验证了有效性.
结论:
- 开发的vGSNN为寻求复杂单调游戏的vGNE提供了有效的分布式解决方案.
- 适应式和滑动模式控制策略提高了稳定性,减少了设计复杂性.
- 该方法在实际场景中得到了验证,证明了它对现实世界多代理系统的适用性.
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