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 https://bit.ly/38hH8t8.

本文提出的 TinyLight，是一个为了极度有限的设备设计的基于深度加  强学习的自适应交通信号控制模型，使用熵最小化目标函数自动减少超图中的边缘，大大降低资源消耗。