  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.
