Can convolutional learning speed up traffic signal AI?
MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control
This paper introduces MacLight, a novel approach to controlling traffic signals using multi-agent reinforcement learning. It uses a convolutional neural network (CNN)-based variational autoencoder (VAE) to create a compact representation of the global traffic state and combines it with local observations for each intersection (agent). This combined representation feeds into a Proximal Policy Optimization (PPO) algorithm for training. A key innovation is the use of a dynamic traffic simulator, enabling testing in scenarios with sudden changes in traffic flow (e.g., road closures).
For LLM-based multi-agent systems, the key takeaways are the use of a VAE for efficient global state representation, which could be adapted for other complex multi-agent environments, and the combination of global and local information within the reinforcement learning framework. The dynamic traffic simulation methodology also offers a valuable approach for creating realistic and challenging training scenarios. Finally, the parallel processing capabilities due to avoiding graph-based methods are particularly relevant for scaling up LLM-based multi-agent systems.