离散时间大种群随机多智能体系统的线性二次分散动态博弈

研究具有耦合二次型随机性能指标的离散时间大种群随机多智能体系统的分散博弈问题.系统所受的噪声干扰为条件二阶矩有界的鞅差序列,比以往研究所考虑的高斯白噪声情形更具有广泛性.采用状态聚集方法构造了对种群状态平均的估计,基于Nash必然等价原理设计了分散控制律,并利用概率极限理论分析了闭环系统的稳定性和最优性.主要结果包括(1)证明了对种群状态的平均的估计在某种范数意义下的强一致性,即种群状态的平均与其估计值之间的误差在该范数意义下将随系统个体数N的增加几乎必然收敛于0;(2)证明了闭环系统的几乎必然一致稳定性,即系统的稳定性与种群个体数N无关;(3)证明了所设计的分散控制律是几乎必然渐近Nash均衡策略.

Linear Quadratic Decentralized Dynamic Games for Large PopulationDiscrete-Time Stochastic Multi-Agent Systems

In this paper, decentralized games of discrete-time large population stochastic multi-agent systems are considered under a coupled quadratic performance index. Based on the state aggregation method, the estimate of the population state average is constructed, with which and the Nash certainty equivalence principle, the decentralized control law is designed. By the probability limit theory, the stability and optimality of closed-loop system is analyzed. The main resultsare: (1) The estimate of the PSA is shown to be strongly consistent in some norm sense, which implies that the estimation error is convergent to zero almost surely as the number of agents increases to infinity. (2) The closed-loop system is almost surely uniformly stable, in other words, the stability is independent of the number of system agents. (3) The decentralized control law is an almost surely asymptotic Nash equilibrium strategy.