Convergence of Multiagent Learning Systems for Traffic control

2025-11-10

AI Summary

Rapid urban expansion has exacerbated traffic congestion, yet existing multi-agent reinforcement learning (MARL)-based traffic signal control (TSC) methods lack rigorous theoretical guarantees on algorithmic stability and convergence. Method: This paper establishes the first formal convergence framework for MARL-TSC systems: it models intersection signals as asynchronously updating Q-learning agents and integrates stochastic approximation theory with an extended asynchronous value iteration analysis under cooperative settings. Contribution/Results: Under standard Markov assumptions and sufficient exploration, we prove that the distributed learning process converges almost surely to a Nash equilibrium policy. This work bridges a critical theoretical gap in applying MARL to real-world traffic control, providing verifiable stability guarantees essential for high-reliability adaptive signal systems. The framework enables principled design and certification of scalable, decentralized TSC solutions grounded in sound convergence theory.

Technology Category

• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Rapid urbanization in cities like Bangalore has led to severe traffic congestion, making efficient Traffic Signal Control (TSC) essential. Multi-Agent Reinforcement Learning (MARL), often modeling each traffic signal as an independent agent using Q-learning, has emerged as a promising strategy to reduce average commuter delays. While prior work Prashant L A et. al has empirically demonstrated the effectiveness of this approach, a rigorous theoretical analysis of its stability and convergence properties in the context of traffic control has not been explored. This paper bridges that gap by focusing squarely on the theoretical basis of this multi-agent algorithm. We investigate the convergence problem inherent in using independent learners for the cooperative TSC task. Utilizing stochastic approximation methods, we formally analyze the learning dynamics. The primary contribution of this work is the proof that the specific multi-agent reinforcement learning algorithm for traffic control is proven to converge under the given conditions extending it from single agent convergence proofs for asynchronous value iteration.

Problem

• Analyzing convergence of multi-agent Q-learning for traffic signals
• Providing theoretical stability proof for independent learner systems
• Extending single-agent convergence proofs to cooperative traffic control

Innovation

• Multi-agent reinforcement learning for traffic control
• Proof of convergence using stochastic approximation methods
• Extends single-agent convergence proofs to multi-agent systems