多集群博弈中投影梯度跟踪及其在电源管理中的应用
Projected gradient-tracking in multi-cluster games and its application to power management
摘要：
我们关注的是一个分布式的方法来解决多集群游戏中出现的多智能体系统。在这样的游戏中，代理人被分成不同的集群。属于同一个集群的智能体相互合作，以实现一个共同的集群目标，而集群之间进行非合作博弈。为了能够处理信息的稀疏性，因为每个代理只知道问题的特定部分，我们将梯度跟踪和共识方法用于信息分配到一个算法中，该算法可以在单次运行中解决合作和非合作问题。问题的约束条件考虑到相应的投影算子和线性收敛性证明了适当的常数步长。该算法被应用到一天前的电源管理问题，提出了作为一个多集群的游戏，其效率是通过模拟证明。
英文摘要：
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.
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