TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources
This work addresses the challenge of implementing adaptive traffic signal control in resource-constrained environments, such as standalone microcontrollers, which is incremental as it adapts existing DRL methods for efficiency.
The authors tackled the problem of deploying deep reinforcement learning for adaptive traffic signal control on devices with extremely limited resources, proposing TinyLight, which achieves competitive performance while operating on a microcontroller with only 2KB RAM and 32KB ROM.
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}.