In recent years, Intelligent Transportation Systems are leveraging the power of increased
sensory coverage and available computing power to deliver data-intensive solutions
achieving higher levels of performance than traditional systems. Within Traffic Signal
Control (TSC), this has allowed the emergence of Machine Learning (ML) based systems.
Among this group, Reinforcement Learning (RL) approaches have performed particularly
well. Given the lack of industry standards in ML for TSC, literature exploring RL often lacks
comparison against commercially available systems and straightforward formulations of
how the agents operate. Here we attempt to bridge that gap. We propose three different
architectures for RL based agents and compare them against currently used commercial
systems MOVA, SurTrac and Cyclic controllers and provide pseudo-code for them. The
agents use variations of Deep Q-Learning (Double Q Learning, Duelling Architectures and
Prioritised Experience Replay) and Actor Critic agents, using states and rewards based on
queue length measurements. Their performance is compared in across different map
scenarios with variable demand, assessing them in terms of the global delay generated by all
vehicles. We find that the RL-based systems can significantly and consistently achieve lower
delays when compared with traditional and existing commercial systems.
