Citation

Yang S, Yang B, Kang Z, Deng L. Neural. Netw. 2021; 139: 265-277.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.neunet.2021.03.015

PMID

unavailable

Abstract

Multi-agent deep reinforcement learning (MDRL) has been widely applied in multi-intersection traffic signal control. The MDRL algorithms produce the decentralized cooperative traffic-signal policies via specialized multi-agent settings in certain traffic networks. However, the state-of-the-art MDRL algorithms seem to have some drawbacks. (1) It is desirable that the traffic-signal policies can be smoothly transferred to diverse traffic networks, however, the adopted specialized multi-agent settings hinder the traffic-signal policies to transfer and generalize to new traffic networks. (2) Existing MDRL algorithms which are based on deep neural networks cannot flexibly tackle a time-varying number of vehicles traversing the traffic networks. (3) Existing MDRL algorithms which are based on homogeneous graph neural networks fail to capture the heterogeneous features of objects in traffic networks. Motivated by the above observations, in this paper, we propose an algorithm, referred to as Inductive Heterogeneous Graph Multi-agent Actor-critic (IHG-MA) algorithm, for multi-intersection traffic signal control. The proposed IHG-MA algorithm has two features: (1) It conducts representation learning using a proposed inductive heterogeneous graph neural network (IHG), which is an inductive algorithm. The proposed IHG algorithm can generate embeddings for previously unseen nodes (e.g., new entry vehicles) and new graphs (e.g., new traffic networks). But unlike the algorithms based on the homogeneous graph neural network, IHG algorithm not only encodes heterogeneous features of each node, but also encodes heterogeneous structural (graph) information. (2) It also conducts policy learning using a proposed multi-agent actor-critic(MA), which is a decentralized cooperative framework. The proposed MA framework employs the final embeddings to compute the Q-value and policy, and then optimizes the whole algorithm via the Q-value and policy loss. Experimental results on different traffic datasets illustrate that IHG-MA algorithm outperforms the state-of-the-art algorithms in terms of multiple traffic metrics, which seems to be a new promising algorithm for multi-intersection traffic signal control.

Language: en

Keywords

Cooperative traffic signal control; Heterogeneous graph neural network; Inductive heterogeneous graph representation learning; Multi-agent reinforcement learning; Transfer learning