MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

arXiv:2101.00746v5 Announce Type: replace
Abstract: Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated promising performance where each traffic signal is regarded as an agent. However, there are still several challenges that may limit its large-scale application in the real world. To make the policy learned from a training scenario generalizable to new unseen …

abstract arxiv city control cs.ai cs.lg cs.ma decentralized efficiency intrinsic meta performance reinforcement reinforcement learning signal traffic type