Can hypergraphs improve traffic signal control?
Towards Multi-agent Policy-based Directed Hypergraph Learning for Traffic Signal Control
September 10, 2024
https://arxiv.org/pdf/2409.05037This paper proposes a new method for controlling traffic signals in a city using AI agents. Instead of treating each intersection in isolation, the method utilizes directed hypergraphs to capture complex relationships between different parts of the road network.
This is relevant to LLM-based multi-agent systems because:
- It demonstrates the value of moving beyond simple pairwise relationships between agents to leverage higher-order interactions within a system.
- It showcases the potential of combining hypergraph learning with reinforcement learning (specifically MA-PPO) for improved coordination in multi-agent environments like traffic control, which could translate to other LLM-based applications.