Can MARL optimize traffic signals under real-world constraints?
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
April 1, 2025
https://arxiv.org/pdf/2503.23626This paper proposes a new algorithm, MAPPO-LCE (Multi-Agent Proximal Policy Optimization with Lagrange Cost Estimator), for controlling traffic signals using multi-agent reinforcement learning. It aims to improve traffic flow by having each intersection act as an independent agent that learns to optimize traffic light timing while adhering to real-world constraints like maximum green light times and preventing frequent phase skipping.
Key points for LLM-based multi-agent systems:
- Constrained Optimization: MAPPO-LCE uses Lagrange multipliers and a cost estimator to balance reward maximization (efficient traffic flow) with constraint satisfaction (realistic signal timing). This is relevant to LLM agents where adhering to constraints (e.g., safety, fairness, fact-checking) is crucial.
- Decentralized Control: Each intersection operates as an independent agent, learning its own policy based on local observations. This decentralized approach can increase scalability in complex multi-agent systems, similar to how LLMs can be deployed as independent agents in collaborative tasks.
- Real-world constraints: The incorporation of real-world traffic constraints demonstrates the importance of grounding LLM agents in practical limitations, ensuring that their actions are relevant and feasible in real-world scenarios. This aligns with making LLM outputs aligned with human values and expectations.
- Scalability: The success of MAPPO-LCE in larger, more complex traffic simulations suggests the potential for applying similar constrained MARL approaches to scalable LLM-based multi-agent applications. This is directly related to the ongoing interest in developing large-scale collaborative LLM systems.