  In mobile crowdsourcing (MCS), the platform selects participants to complete
location-aware tasks from the recruiters aiming to achieve multiple goals
(e.g., profit maximization, energy efficiency, and fairness). However,
different MCS systems have different goals and there are possibly conflicting
goals even in one MCS system. Therefore, it is crucial to design a participant
selection algorithm that applies to different MCS systems to achieve multiple
goals. To deal with this issue, we formulate the participant selection problem
as a reinforcement learning problem and propose to solve it with a novel
method, which we call auxiliary-task based deep reinforcement learning (ADRL).
We use transformers to extract representations from the context of the MCS
system and a pointer network to deal with the combinatorial optimization
problem. To improve the sample efficiency, we adopt an auxiliary-task training
process that trains the network to predict the imminent tasks from the
recruiters, which facilitates the embedding learning of the deep learning
model. Additionally, we release a simulated environment on a specific MCS task,
the ride-sharing task, and conduct extensive performance evaluations in this
environment. The experimental results demonstrate that ADRL outperforms and
improves sample efficiency over other well-recognized baselines in various
settings.
