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Distributed agent-based deep reinforcement learning for large scale traffic signal control

Journal Article


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.

Publication Date


  • 2022

Citation


  • Wu, Q., Wu, J., Shen, J., Du, B., Telikani, A., Fahmideh, M., & Liang, C. (2022). Distributed agent-based deep reinforcement learning for large scale traffic signal control. Knowledge-Based Systems, 241. doi:10.1016/j.knosys.2022.108304

Scopus Eid


  • 2-s2.0-85124420015

Volume


  • 241

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.

Publication Date


  • 2022

Citation


  • Wu, Q., Wu, J., Shen, J., Du, B., Telikani, A., Fahmideh, M., & Liang, C. (2022). Distributed agent-based deep reinforcement learning for large scale traffic signal control. Knowledge-Based Systems, 241. doi:10.1016/j.knosys.2022.108304

Scopus Eid


  • 2-s2.0-85124420015

Volume


  • 241