3 papers - avg viability 7.0
A Mixture-of-Experts Reinforcement Learning framework that uses predictive clustering to adapt traffic signal control to diverse and dynamic urban traffic patterns, outperforming existing methods.
A decentralized AI framework for optimizing city-wide traffic signals using novel state representation and neighbor-aware policy optimization.
A robust multi-agent reinforcement learning framework that optimizes traffic signal control for dynamic traffic conditions.