移动众包中基于辅助任务的深度强化学习参与者选择问题
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing
摘要：
在移动众包（MCS）中，平台选择参与者来完成来自招聘人员的位置感知任务，旨在实现多个目标（例如，利润最大化、能源效率和公平）。然而，不同的MCS系统具有不同的目标，并且即使在一个MCS系统中也可能存在冲突的目标。因此，设计一种适用于不同MCS系统的参与者选择算法以实现多个目标是至关重要的。为了解决这个问题，我们将参与者选择问题表示为强化学习问题，并提出了一种新的方法来解决这个问题，我们称之为基于任务的深度强化学习（ADRL）。我们使用Transformers从MCS系统的上下文中提取表示，并使用指针网络来处理组合优化问题。为了提高样本效率，我们采用了一个任务训练过程，训练网络预测招聘人员即将完成的任务，这有利于深度学习模型的嵌入式学习。此外，我们发布了一个模拟的环境中的一个特定的MCS任务，共乘任务，并在此环境中进行广泛的性能评估。实验结果表明，ADRL优于和提高样本效率比其他公认的基线在各种设置。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
