A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control

This paper proposes a robust Multi-Agent Reinforcement Learning (MARL) framework for Traffic Signal Control, validated in the Vissim traffic simulator. It addresses generalization challenges through adaptive state representation, a novel reward function, and agent communication. The framework shows superior performance in diverse traffic scenarios.