  This article proposes a methodology for the development of adaptive traffic
signal controllers using reinforcement learning. Our methodology addresses the
lack of standardization in the literature that renders the comparison of
approaches in different works meaningless, due to differences in metrics,
environments, and even experimental design and methodology. The proposed
methodology thus comprises all the steps necessary to develop, deploy and
evaluate an adaptive traffic signal controller -- from simulation setup to
problem formulation and experimental design. We illustrate the proposed
methodology in two simple scenarios, highlighting how its different steps
address limitations found in the current literature.
