How Well Do Reinforcement Learning Approaches Cope With Disruptions? The Case of Traffic Signal Control

Marcin Korecki, Damian Dailisan, Dirk Helbing

Abstract: Data-driven and machine-learning-based methods are increasingly used in attempts to master the challenges of the world. But are they really the best approaches to manage complex dynamical systems? Our aim is to gain more insights into this question by studying various popular reinforcement learning methods for traffic signal control, namely in disrupted scenarios characterized by significant, unpredictable variations. The results are expected to be relevant in subject areas ranging from traffic physics to transportation theory, from dynamics in networks to complex systems, from control theory to self-organization, and from adaptive heuristics to machine learning.