Reinforcement Learning and Regret Bounds for Admission Control

The expected regret of any reinforcement learning algorithm is lower bounded by $\Omega\left(\sqrt{DXAT}\right)$ for undiscounted returns, where $D$ is the diameter of the Markov decision process, $X$ the size of the state space, $A$ the size of the action space and $T$ the number of time steps. However, this lower bound is general. A smaller regret can be obtained by taking into account some specific knowledge of the problem structure. In this article, we consider an admission control problem to an $M/M/c/S$ queue with $m$ job classes and class-dependent rewards and holding costs. Queuing systems often have a diameter that is exponential in the buffer size $S$, making the previous lower bound prohibitive for any practical use. We propose an algorithm inspired by UCRL2, and use the structure of the problem to upper bound the expected total regret by $O(S\log T + \sqrt{mT \log T})$ in the finite server case. In the infinite server case, we prove that the dependence of the regret on $S$ disappears.

Deep learning-based person re-identification methods: A survey and outlook of recent works

In recent years, with the increasing demand for public safety and the rapid development of intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot research topics in the field of computer vision. The main research goal of person Re-ID is to retrieve persons with the same identity from different cameras. However, traditional person Re-ID methods require manual marking of person targets, which consumes a lot of labor cost. With the widespread application of deep neural networks in the field of computer vision, a large number of deep learning-based person Re-ID methods have emerged. Therefore, this paper is to facilitate researchers to better understand the latest research results and the future trends in the field. Firstly, we summarize the main study of several recently published person re-identification surveys and try to fill the gaps between them. Secondly, We propose a multi-dimensional taxonomy to categorize the most current deep learning-based person Re-ID methods according to different characteristics, including methods for deep metric learning, local feature learning, generate adversarial networks, sequence feature learning and graph convolutional networks. Furthermore, we subdivide the above five categories according to their technique types, discussing and comparing the experimental performance of part subcategories. Finally, we conclude this paper and discuss future research directions for person Re-ID.