Deep reinforcement learning for traffic signal control with consistent state and reward design approach

Bouktif, Salah, Cheniki, Abderraouf, Ouni, Ali et El-Sayed, Hesham. 2023. « Deep reinforcement learning for traffic signal control with consistent state and reward design approach ». Knowledge-Based Systems, vol. 267.
Compte des citations dans Scopus : 14.

Prévisualisation

PDF Ouni-A-2023-26312.pdf - Version publiée Licence d'utilisation : Creative Commons CC BY-NC-ND. Télécharger (1MB) | Prévisualisation

Résumé

Intelligent Transportation Systems are essential due to the increased number of traffic congestion problems and challenges nowadays. Traffic Signal Control (TSC) plays a critical role in optimizing the traffic flow and mitigating the congestion within the urban areas. Various research works have been conducted to enhance the behavior of TSCs at intersections and subsequently reduce the traffic congestion. Researchers recently leveraged Deep Learning (DL) and Reinforcement Learning (RL) techniques to optimize TSCs. In RL framework, the agent interacts with surrounding world through states, rewards and actions. The formulation of these key elements is crucial as they impact the way the RL agent behaves and optimizes its policy. However, most of existing frameworks rely on hand-crafted state and reward designs, restricting the RL agent from acting optimally. In this paper, we propose a novel approach to better formulate state and reward definitions in order to boost the performance of the traffic signal controller agent. The intuitive idea is to define both state and reward in a consistent and straightforward manner. We advocate that such a design approach helps achieving training stability and hence provides a rapid convergence to derive best policies. We consider the double deep Q-Network (DDQN) along with prioritized experience replay (PER) for the agent architecture. To evaluate the performance of our approach, we conduct series of simulations using the Simulation of Urban MObility (SUMO) environment. The statistical analysis of our results show that the performance of our proposal outperforms the state-of-the-art state and reward design approaches

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur Ouni, Ali
Affiliation:	Génie logiciel et des technologies de l'information
Date de dépôt:	06 avr. 2023 21:54
Dernière modification:	12 avr. 2023 18:21
URI:	https://espace2.etsmtl.ca/id/eprint/26312

Actions (Authentification requise)

Dernière vérification avant le dépôt