针对具有规定的性能的多代理系统,基于自适应性批判性学习的最佳双边共识
概括
本研究引入了一个适应性批评学习方案,以实现最佳的双边共识,确保用户定义的性能. 新方法简化了控制结构,并保证了通过模拟验证的性能限制.
科学领域:
- 控制系统工程 控制系统工程
- 分布式系统 分布式系统
- 机器学习 机器学习
背景情况:
- 在分布式双边最佳共识中实现用户预定义的性能是当前复杂的标识符-行为者-关键框架的挑战.
- 在当前的共识计划中,规定的绩效保证往往没有得到满足.
研究的目的:
- 开发一个基于自适应批判学习 (ACL) 的简化,最佳的双方共识方案.
- 为了确保用户预定义的结算时间和独立于初始条件的稳定精度.
- 从现有的控制结构中去除复杂的标识符和行为者网络.
主要方法:
- 将一个新的错误缩放函数集成到成本函数中.
- 后退框架与ACL和综合强化学习 (IRL) 的结合.
- 开发一个只有批评者的控制器结构,通过梯度下降和体验重复获得的适应性规律.
主要成果:
- 实现了一个计算节约学习机制,以实现最佳的双边共识.
- 封闭循环系统的错误变量被证明是统一的最终边界 (UUB).
- 双边共识进化是限制在所有有限的初始条件的用户规定的边界内.
结论:
- 拟议的基于ACL的方案有效地实现了最佳的双边共识,并保证了业绩.
- 只有批评者的控制器简化了系统,同时保持了性能和稳定性.
- 模拟结果证实了该方法的有效性和稳定性.
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