Intelligent Traffic Light Control System (ITLCS) is a typical Multi-Agent
System (MAS), which comprises multiple roads and traffic lights.Constructing a
model of MAS for ITLCS is the basis to alleviate traffic congestion. Existing
approaches of MAS are largely based on Multi-Agent Deep Reinforcement Learning
(MADRL). Although the Deep Neural Network (DNN) of MABRL is effective, the
training time is long, and the parameters are difficult to trace. Recently,
Broad Learning Systems (BLS) provided a selective way for learning in the deep
neural networks by a flat network. Moreover, Broad Reinforcement Learning (BRL)
extends BLS in Single Agent Deep Reinforcement Learning (SADRL) problem with
promising results. However, BRL does not focus on the intricate structures and
interaction of agents. Motivated by the feature of MADRL and the issue of BRL,
we propose a Multi-Agent Broad Reinforcement Learning (MABRL) framework to
explore the function of BLS in MAS. Firstly, unlike most existing MADRL
approaches, which use a series of deep neural networks structures, we model
each agent with broad networks. Then, we introduce a dynamic self-cycling
interaction mechanism to confirm the "3W" information: When to interact, Which
agents need to consider, What information to transmit. Finally, we do the
experiments based on the intelligent traffic light control scenario. We compare
the MABRL approach with six different approaches, and experimental results on
three datasets verify the effectiveness of MABRL.

本文提出了一种基于多智能体广义深度强化学习框架（MABRL）的智能交通信号控制系统，使用了广泛学习系统和动态自循环交互机制，通过与其他六种算法的比较，证明了其在解决交通拥堵问题上的有效性。