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首页> 外文期刊>IEEE Transactions on Automatic Control >Deep Teams: Decentralized Decision Making With Finite and Infinite Number of Agents
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Deep Teams: Decentralized Decision Making With Finite and Infinite Number of Agents

机译：深队：分散决策，具有有限和无限的代理商

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Inspired by the concepts of deep learning in artificial intelligence and fairness in behavioral economics, we introduce deep teams in this article. In such systems, agents are partitioned into a few subpopulations so that the dynamics and cost of agents in each subpopulation is invariant to the indexing of agents. The goal of agents is to minimize a common cost function in such a manner that the agents in each subpopulation are not discriminated or privileged by the way they are indexed. Two nonclassical information structures are studied. In the first one, each agent observes its local state as well as the empirical distribution of the states of agents in each subpopulation, called deep state, whereas in the second one, the deep states of a subset (possibly all) of subpopulations are not observed. Novel dynamic programs are developed to identify globally optimal and suboptimal solutions for the first and second information structures, respectively. The computational complexity of finding the optimal solution in both space and time is polynomial (rather than exponential) with respect to the number of agents in each subpopulation and is linear (rather than exponential) with respect to the control horizon. This complexity is further reduced in time by introducing a forward equation, which we call deep Chapman-Kolmogorov equation, described by multiple convolutional layers of binomial probability distributions. Two different prices are defined for computation and communication, and it is shown that under mild conditions they converge to zero as the number of quantization levels and the number of agents tend to infinity. In addition, the main results are extended to infinite-horizon discounted models and arbitrarily asymmetric cost functions. Finally, a service management example with 200 users is presented.
机译：灵感灵感来自人工智能和行为经济学中的人工智能和公平的概念，我们介绍了本文的深度团队。在这样的系统中，试剂被分成几个亚步骤，使得每种亚潜水病中的药剂的动态和成本是不变的代理的索引。代理的目标是以最大限度地减少共同的成本函数，使得每种亚居民的药物不会通过它们被编制的方式歧视或特权。研究了两个非分化信息结构。在第一个方面，每个试剂观察其当地州以及每个亚父潜水病的药剂状态的经验分布，称为深处状态，而在第二个状态下，子集（可能全部）群体的深度状态不是观察到的。开发了新颖的动态程序，以分别为第一和第二信息结构识别全局最佳和次优的解决方案。在两个空间和时间中找到最佳解决方案的计算复杂度是关于每个亚泊素中的代理的数量的多项式（而不是指数的），并且相对于控制地平线是线性（而不是指数的）。通过引入前向等式的前向等式进一步减少了这种复杂性，该转发方程是由多个卷积层的二项式概率分布描述的深度查曼 - kolmogorov方程。定义了两种不同的价格用于计算和通信，并显示在温和条件下，它们随着量化水平的数量和代理的数量倾向于无穷大而零化。此外，主要结果延伸到无限的地平线折扣型号和任意不对称的成本函数。最后，展示了具有200个用户的服务管理示例。

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