Existing traffic signal control systems rely on oversimplified rule-based
methods, and even RL-based methods are often suboptimal and unstable. To
address this, we propose a cooperative multi-objective architecture called
Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MOMA-DDPG),
which estimates multiple reward terms for traffic signal control optimization
using age-decaying weights. Our approach involves two types of agents: one
focuses on optimizing local traffic at each intersection, while the other aims
to optimize global traffic throughput. We evaluate our method using real-world
traffic data collected from an Asian country's traffic cameras. Despite the
inclusion of a global agent, our solution remains decentralized as this agent
is no longer necessary during the inference stage. Our results demonstrate the
effectiveness of MOMA-DDPG, outperforming state-of-the-art methods across all
performance metrics. Additionally, our proposed system minimizes both waiting
time and carbon emissions. Notably, this paper is the first to link carbon
emissions and global agents in traffic signal control.

提出一种合作的多目标架构 MOMA-DDPG 来优化交通信号控制，包含两种类型的代理人，一个代理人关注于优化本地交通，而另一个代理人旨在优化全局交通吞吐量，实验结果表明该方法优于现有的方法，可以最小化等待时间和碳排放。