Learning an Interpretable Traffic Signal Control Policy

Signalized intersections are managed by controllers that assign right of way (green, yellow, and red lights) to non-conflicting directions. Optimizing the actuation policy of such controllers is expected to alleviate traffic congestion and its adverse impact. Given such a safety-critical domain, the affiliated actuation policy is required to be interpretable in a way that can be understood and regulated by a human. This paper presents and analyzes several on-line optimization techniques for tuning interpretable control functions. Although these techniques are defined in a general way, this paper assumes a specific class of interpretable control functions (polynomial functions) for analysis purposes. We show that such an interpretable policy function can be as effective as a deep neural network for approximating an optimized signal actuation policy. We present empirical evidence that supports the use of value-based reinforcement learning for on-line training of the control function. Specifically, we present and study three variants of the Deep Q-learning algorithm that allow the training of an interpretable policy function. Our Deep Regulatable Hardmax Q-learning}variant is shown to be particularly effective in optimizing our interpretable actuation policy, resulting in up to 19.4% reduced vehicles delay compared to commonly deployed actuated signal controllers.

This code was tested on Ubuntu 18.04 using SUMO 1.0.1 implementing the experiments described in the paper. You must set the environment variable 'SUMO_HOME' to your sumo installation directory.

If you find this work useful in your research, please cite:

@inproceedings{ault2020learning,
	title={Learning an Interpretable Traffic Signal Control Policy},
	author={James Ault and Josiah Hanna and Guni Sharon},
	booktitle={Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2020)},
	location = {Auckland, New Zealand},
	month = {May},
	year={2020},
	organization={International Foundation for Autonomous Agents and Multiagent Systems}
}