Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing

TL;DR

This paper introduces ADRL, a novel deep reinforcement learning approach utilizing auxiliary tasks and transformers to optimize participant selection in mobile crowdsourcing, effectively balancing multiple goals.

Contribution

It proposes a new auxiliary-task based deep reinforcement learning method with transformer and pointer networks for flexible, efficient participant selection in diverse MCS systems.

Findings

ADRL outperforms baseline methods in sample efficiency and effectiveness.

The auxiliary-task training improves embedding learning and decision quality.

Experimental results validate ADRL's adaptability to various MCS scenarios.

Abstract

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.