Deep Reinforcement Learning for Task Assignment in Spatial Crowdsourcing and Sensing

With the continuous popularization of smart devices, Spatial Crowdsourcing(SC) came into being a novel paradigm of collecting smart sensing. As a core issue, the task assignment aims to arrange tasks to suitable workers for different objectives. However, most existing approaches matched tasks and workers under the premise of already known their number, while the arrival information of crowdsourcing participants cannot be understood in advance for their dynamically appearing. We fill this gap by proposing a Dynamic Task Assignment Framework based on Prediction and Adaptive Batching (DTAF-PAB). Specifically, we first predict the number of coming tasks and/or workers by a deep learning model Gated Recurrent Unit (GRU). Then, the adaptive batching strategies, based on Deep Q Network (DQN) and Double DQN (DDQN), are investigated to address the problem of adaptive and dynamic batching for task assignment. We design a deep reinforcement learning (DRL)-based algorithm to improve the overall benefit of the assignment. Finally, we use real-world datasets to evaluate the competitiveness of DTAF-PAB, and the experimental results show that the proposed framework is superior to other existing methods in terms of both predictive performance and crowdsourcing platform benefit.