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Distributed agent-based deep reinforcement learning for large scale traffic signal control.
- Source :
-
Knowledge-Based Systems . Apr2022, Vol. 241, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- Traffic signal control (TSC) is an established yet challenging engineering solution that alleviates traffic congestion by coordinating vehicles' movements at road intersections. Theoretically, reinforcement learning (RL) is a promising method for adaptive TSC in complex urban traffic networks. However, current TSC systems still rely heavily on simplified rule-based methods in practice. In this paper, we propose: (1) two game theory-aided RL algorithms leveraging Nash Equilibrium and RL, namely Nash Advantage Actor–Critic (Nash-A2C) and Nash Asynchronous Advantage Actor–Critic (Nash-A3C); (2) a distributed computing Internet of Things (IoT) architecture for traffic simulation, which is more suitable for distributed TSC methods like the Nash-A3C deployment in its fog layer. We apply both methods in our computing architecture and obtain better performance than benchmark TSC methods by 22.1% and 9.7% reduction of congestion time and network delay, respectively. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 241
- Database :
- Academic Search Index
- Journal :
- Knowledge-Based Systems
- Publication Type :
- Academic Journal
- Accession number :
- 155456181
- Full Text :
- https://doi.org/10.1016/j.knosys.2022.108304