Traffic signal control is important in intelligent transportation system, of which cooperative control is difficult to realize but yet vital. Many methods model multi-intersection traffic networks as grids and address the problem using multi-agent reinforcement learning (RL). Despite these existing studies, there is an opportunity to further enhance our understanding of the connectivity and globality of the traffic networks by capturing the spatiotemporal traffic information with efficient neural networks in deep RL. In this paper, we propose a novel multi-agent actor-critic framework based on an interpretable influence mechanism with a centralized learning and decentralized execution method. Specifically, we first construct an actor-critic framework, for which the piecewise linear neural network (PWLNN), named biased ReLU (BReLU), is used as the function approximator to obtain a more accurate and theoretically grounded approximation. Finally, our proposed framework is validated on two synthetic traffic networks to coordinate signal control between intersections, achieving lower traffic delays across the entire traffic network compared to state-of-the-art (SOTA) performance.

翻译：交通信号控制在智能交通系统中至关重要，其中协同控制难以实现但却非常关键。许多方法将多交叉口交通网络建模为网格，并采用多智能体强化学习来解决该问题。尽管已有这些研究，通过深度强化学习中高效的神经网络捕捉时空交通信息，仍有机会进一步加深对交通网络连通性和全局性的理解。本文提出了一种新颖的多智能体演员-评论家框架，该框架基于可解释的影响机制，采用集中式学习与分散式执行方法。具体而言，我们首先构建了一个演员-评论家框架，其中使用分段线性神经网络（PWLNN），即偏置ReLU（BReLU），作为函数逼近器，以获得更精确且理论上可靠的逼近。最后，我们在两个合成交通网络上验证了所提框架在交叉口信号控制协调方面的有效性，与最先进技术相比，实现了整个交通网络中更低的交通延迟。