论文标题
Tinylight：具有极有限资源的设备上的自适应交通信号控制
TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources
论文作者
论文摘要
深度强化学习（DRL）的最新进展在很大程度上促进了自适应交通信号控制（ATSC）的性能。但是，关于实施，大多数作品在存储和计算方面都很麻烦。这阻碍了他们在资源有限的方案上的部署。在这项工作中，我们提出了Tinylight，这是第一个基于DRL的ATSC模型，该模型是为资源极为有限的设备而设计的。 Tinylight首先构建了一个超级雕像，可以将丰富的候选功能与一组轻度加权网络块相关联。然后，为了减少模型的资源消耗，我们会以新型的熵最小化的目标函数自动在超级雕像中烧蚀。这使Tinylight可以使用仅2KB RAM和32KB ROM的独立微控制器。我们在多个道路网络上评估Tinylight，这些网络具有真实世界的交通需求。实验表明，即使资源极为有限，Tinylight仍然可以达到竞争性能。可以在\ url {https://bit.ly/38HH8T8}上找到此工作的源代码和附录。
Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, most works are cumbersome in terms of storage and computation. This hinders their deployment on scenarios where resources are limited. In this work, we propose TinyLight, the first DRL-based ATSC model that is designed for devices with extremely limited resources. TinyLight first constructs a super-graph to associate a rich set of candidate features with a group of light-weighted network blocks. Then, to diminish the model's resource consumption, we ablate edges in the super-graph automatically with a novel entropy-minimized objective function. This enables TinyLight to work on a standalone microcontroller with merely 2KB RAM and 32KB ROM. We evaluate TinyLight on multiple road networks with real-world traffic demands. Experiments show that even with extremely limited resources, TinyLight still achieves competitive performance. The source code and appendix of this work can be found at \url{https://bit.ly/38hH8t8}.
