Decentralized Task Assignment for Mobile Crowdsensing With Multi-Agent Deep Reinforcement Learning

Abstract

Task assignment is a fundamental research problem in mobile crowdsensing (MCS) since it directly determines an MCS system's practicality and economic value. Due to the complex dynamics of tasks and workers, task assignment problems are usually NP-hard, and approximation-based methods are preferred to impractical optimal methods. In the literature, a graph neural network-based deep reinforcement learning (GDRL) method is proposed in Xu and Song (2022) to solve routing problems in MCS and shows high performance and time efficiency. However, GDRL, as a centralized method, has to cope with the limitation in scalability and the challenge of privacy protection. In this article, we propose a multi-agent deep reinforcement learning-based method named communication-QMIX-based multi-agent DRL (CQDRL) to solve a task assignment problem in a decentralized fashion. The CQDRL method not only inherits the merits of GDRL over handcrafted heuristic and metaheuristic methods but also exploits computation potentials in mobile devices and protects workers' privacy with a decentralized decision-making scheme. Our extensive experiments show that the CQDRL method can achieve significantly better performance than other traditional methods and performs fairly close to the centralized GDRL method.