Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

A novel Meta Variationally Intrinsic Motivated (MetaVIM) reinforcement learning method is proposed to learn decentralized policies for traffic signal control that consider neighbor information in a latent way, enabling effective and generalizable control in large-scale road networks.