We study the problem of control policy design for decentralized state-feedback linear quadratic control with a partially nested information structure, when the system model is unknown. We propose a model-based learning solution, which consists of two steps. First, we estimate the unknown system model from a single system trajectory of finite length, using least squares estimation. Next, based on the estimated system model, we design a control policy that satisfies the desired information structure. We show that the suboptimality gap between our control policy and the optimal decentralized control policy (designed using accurate knowledge of the system model) scales linearly with the estimation error of the system model. Using this result, we provide an end-to-end sample complexity result for learning decentralized controllers for a linear quadratic control problem with a partially nested information structure.
翻译：当系统模型未知时,我们研究以部分嵌入式信息结构为部分嵌入式的国家fefack 线性二次控制的控制政策设计问题。 我们提出了一个基于模型的学习解决方案, 由两步组成。 首先, 我们用最小平方估计, 从一个有限长度的单一系统轨迹中估算未知系统模型。 其次, 根据估计系统模型, 我们设计一个符合理想信息结构的控制政策。 我们显示, 我们的控制政策与最佳的分散控制政策( 使用系统模型的准确知识来设计) 之间的次优化差距与系统模型的估计错误线性比例( 使用系统模型的准确知识来设计) 。 我们使用这一结果, 我们提供了一个端到端的样本复杂性结果, 用于学习具有部分嵌入式信息结构的线性二次控制问题的分散控制器。
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