TSLib: A Unified Traffic Signal Control Framework Using Deep Reinforcement Learning and Benchmarking

Toan V. Tran, Thanh-Nam Doan, Mina Sartipi

Abstract: The volume and velocity of traffic data have in-creased dramatically due to the wide adoption of new technologies such as cameras, Internet-of-Thing devices, and vehicular net-works. That data can help us to optimize Traffic Signal Controls (TSCs) by using adaptive algorithms. Some direct applications of these algorithms are reducing the CO2 emission, fuel consumption, and traveling time. Recently, Deep Reinforcement Learning (DRL) methods are the de-facto solution due to its ability to handle big data with high performance. However, most open source codes and frameworks for TSCs using DRL algorithms have limited flexibility. That causes a difficulty to reuse the codebases for new contexts. Therefore, it will be difficult to have a benchmark for TSCs using different optimization algorithms. For this reason, our paper introduces TSLib – a Python framework for fast prototyping TSCs. Specifically, TSLib is designed as a modular system with high reusability so that researchers can quickly implement and evaluate new ideas of TSCs. Moreover, our work offers a comprehensive implementation of some well known TSCs algorithm including both traditional and DRL-based methods as well as their performance measurements.