基于可解释影响机制和偏置ReLU近似的多智能体强化交通信号控制
Multi-agent Reinforcement Traffic Signal Control based on Interpretable Influence Mechanism and Biased ReLU Approximation
AI总结：
本文提出一种基于可解释影响机制和偏置ReLU逼近的多智能体演员-评论家框架，用于协同交通信号控制，在合成网络上实现低于SOTA的交通延迟。
摘要：
交通信号控制在智能交通系统中具有重要意义，其中协同控制难以实现但至关重要。许多方法将多交叉口交通网络建模为网格，并使用多智能体强化学习（RL）来解决该问题。尽管已有这些研究，但通过深度RL中高效的神经网络捕获时空交通信息，仍有机会进一步增强我们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，基于可解释的影响机制，采用集中学习与分散执行的方法。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），称为偏置ReLU（BReLU），作为函数逼近器，以获得更准确且理论上更有依据的逼近。最后，我们在两个合成交通网络上验证了所提出的框架，以协调交叉口之间的信号控制，与最先进（SOTA）性能相比，在整个交通网络上实现了更低的交通延迟。
英文摘要：
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.
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