论文标题
遗憾的是在部分可观察的线性二次控制中最小化
Regret Minimization in Partially Observable Linear Quadratic Control
论文作者
论文摘要
当模型动力学未知的先验动力学时，我们研究了部分可观察到的线性二次控制系统中遗憾最小化的问题。我们提出了ExpCommit，这是一种探索 - 启动算法，该算法了解Markov参数，然后在面对不确定性设计控制器时遵循乐观原理。我们提出了一种新颖的方式来分解遗憾，并为部分可观察到的线性二次控制提供了端到端的Sublerear遗憾上限。最后，我们提供稳定性保证，并为$ \ tilde {\ Mathcal {o}}}（t^{2/3}）$建立遗憾的上限，其中$ t $是问题的时间范围。
We study the problem of regret minimization in partially observable linear quadratic control systems when the model dynamics are unknown a priori. We propose ExpCommit, an explore-then-commit algorithm that learns the model Markov parameters and then follows the principle of optimism in the face of uncertainty to design a controller. We propose a novel way to decompose the regret and provide an end-to-end sublinear regret upper bound for partially observable linear quadratic control. Finally, we provide stability guarantees and establish a regret upper bound of $\tilde{\mathcal{O}}(T^{2/3})$ for ExpCommit, where $T$ is the time horizon of the problem.
