  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.
