论文标题
预计多群集游戏中的梯度跟踪及其在电力管理中的应用
Projected gradient-tracking in multi-cluster games and its application to power management
论文作者
论文摘要
我们关注的是解决多代理系统中出现的多群集游戏的分布式方法。在这样的游戏中，代理分为不同的群集。属于同一集群的代理商相互合作，以实现共同的集群目标，而群集之间进行了不合作的游戏。为了能够处理信息的稀疏性，由于每个代理只知道问题的特定部分，因此我们将梯度跟踪和共识方法结合在一起，以信息分布将信息分配到可以在单个跑道中解决合作和非合作问题的算法。相应的投影操作员考虑了问题的约束，并且在适当的恒定步长尺寸的情况下，线性收敛被证明。该算法应用于作为多群集游戏的日前电力管理问题，并通过模拟证明其效率。
We are concerned with a distributed approach to solve multi-cluster games arising in multi-agent systems. In such games, agents are separated into distinct clusters. The agents belonging to the same cluster cooperate with each other to achieve a common cluster goal while a non-cooperative game is played between the clusters. To be able to deal with the sparsity of information, as each agent only knows a specific part of the problem, we combine gradient-tracking and consensus methods for information distribution into an algorithm that can solve both the cooperative and non-cooperative problem in a single run. The constraints of the problem are taken into account by the corresponding projection operators and linear convergence is proven given an appropriate constant step size. The algorithm is applied to a day-ahead power management problem, posed as a multi-cluster game, and its efficiency is demonstrated by simulations.
