A Fast Decentralized Scheduling Method of Cooperative Localization Based on Actor-Critic Deep Reinforcement

With the emergence of more and more automated vehicles, localization of vehicles has attracted a lot of attention. Among multiple localization methods, cooperative localization is very attractive due to its high coverage and accuracy. However, it is costly and leads to larger delays to exhaustively measure and exchange information between all adjacent vehicles. Thus, it is a challenge to schedule the transmissions for the cooperative localization. In this paper, we describe the cooperative localization as a partially observable Markov process and propose an actorcritic deep reinforcement learning algorithm to bring the vehicles to a given localization accuracy threshold as quickly as possible. The proposed algorithm allows the transmissions to be optimally scheduled in a distributed manner. Simulation results show that, compared with random, greedy, and two existing deep reinforcement learning algorithms, the proposed algorithm has better performance and is more adaptable to large-scale complex networks.