Convergence of Multiagent Learning Systems for Traffic control

TL;DR

This paper provides a rigorous theoretical analysis proving the convergence of multi-agent reinforcement learning algorithms used for traffic signal control in urban environments.

Contribution

It extends existing convergence proofs from single-agent to multi-agent settings specifically for traffic control applications.

Findings

Proves convergence of the multi-agent reinforcement learning algorithm under certain conditions.

Extends single-agent convergence proofs to multi-agent asynchronous value iteration.

Provides a theoretical foundation for the stability of multi-agent traffic control systems.

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.