SIMPLIFICATION OF DEEP REINFORCEMENT LEARNING IN TRAFFIC CONTROL USING THE BONSAI PLATFORM

What is it about?

The paper deals with the problem of traffic light control of road intersections. The authors use a model of a real road junction created in the AnyLogic modeling tool. For two scenarios, there are three simulation experiments performed – fixed-time control, fixed time control after AnyLogic-based optimizations, and dynamic control obtained through the cooperation of the AnyLogic tool and the Bonsai platform, utilizing benefits of deep reinforcement learning. Due to unavailability of real operational data, the model uses simulation data only, with presence and movement of vehicles only (no pedestrians). The optimization problem consists in minimizing the average time that agents (vehicles) must spend in the model, passing the modelled intersection. Another observed parameter is the maximum time of individual vehicles spent in the model. The authors share their practical, mainly methodological, experiences with the simulation process and indicate economic cost needed for training as well.

Featured Image

Photo by David Guenther on Unsplash

Photo by David Guenther on Unsplash

Why is it important?

At present, there are trends to simplify machine learning processes as much as possible to make them accessible to practitioners with no artificial intelligence background and without the need to become data scientists. Project Bonsai represents an easy-to-use connector, that allows to use AnyLogic models connected to the Bonsai platform - a novel approach to machine learning without the need to set any hyper-parameters.