  Applying reinforcement learning (RL) to traffic signal control (TSC) has
become a promising solution. However, most RL-based methods focus solely on
optimization within simulators and give little thought to deployment issues in
the real world. Online RL-based methods, which require interaction with the
environment, are limited in their interactions with the real-world environment.
Additionally, acquiring an offline dataset for offline RL is challenging in the
real world. Moreover, most real-world intersections prefer a cyclical phase
structure. To address these challenges, we propose: (1) a cyclical offline
dataset (COD), designed based on common real-world scenarios to facilitate easy
collection; (2) an offline RL model called DataLight, capable of learning
satisfactory control strategies from the COD; and (3) a method called Arbitrary
To Cyclical (ATC), which can transform most RL-based methods into cyclical
signal control. Extensive experiments using real-world datasets on simulators
demonstrate that: (1) DataLight outperforms most existing methods and achieves
comparable results with the best-performing method; (2) introducing ATC into
some recent RL-based methods achieves satisfactory performance; and (3) COD is
reliable, with DataLight remaining robust even with a small amount of data.
These results suggest that the cyclical offline dataset might be enough for
offline RL for TSC. Our proposed methods make significant contributions to the
TSC field and successfully bridge the gap between simulation experiments and
real-world applications. Our code is released on Github.

Machine Learning;Artificial Intelligence