Mastering Arterial Traffic Signal Control With Multi-Agent Attention-Based Soft Actor-Critic Model

Recent studies have made dozens of attempts to apply multi-agent deep reinforcement learning (MARL) for large-scale traffic signal control. However, most related studies have ignored how to master arterial traffic signal control. We cannot easily extract useful information and search solution space because the arterial traffic control problem has large state-action spaces. Here we tackle these issues by proposing a multi-agent attention-base soft actor-critic (MASAC) model to master arterial traffic control. Specifically, we implement the attention mechanism in the actor and critic network to enhance traffic information extraction ability. More importantly, we are the first to apply the soft actor-critic (SAC) algorithm to train the arterial traffic control model to search more solution spaces. Testing results indicate that the MASAC method significantly outperforms existing MARL algorithms and the multiband-based method. These findings can help researchers to design better model structures for other MARL problems.

Automated summary

Recent studies have attempted to apply multi-agent deep reinforcement learning (MARL) for large-scale traffic signal control but have overlooked arterial traffic signal control. This study proposes a multi-agent attention-based soft actor-critic (MASAC) model to address these issues. The MASAC method significantly outperforms existing MARL algorithms and the multiband-based method according to testing results.