基于深度强化学习的空间众包任务分配策略

doi:10.16451/j.cnki.issn1003-6059.202103001

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Abstract In the traditional dynamic online task allocation strategy, it is difficult to effectively make use of historical data for learning and the impact of current decisions on future revenue is not taken into account. Therefore, a task allocation strategy of spatial crowdsourcing based on deep reinforcement learning is proposed. Firstly, maximizing long-term cumulative income is regarded as an objective function and the task assignment problem is transformed into the solution of Q value of state action and the one-to-one distribution between workers and tasks by modeling from the perspective of a single crowdsourcing worker grounded on Markov decision process. Secondly, the improved deep reinforcement learning algorithm is applied to learn the historical task data offline to construct the prediction model with respect to Q value. Finally, Q value in real time gained by the model in the dynamic online distribution process is regarded as a side weight of KM algorithm. The optimal distribution of global cumulative returns can be achieved. The results of comparative experiment on the real taxi travel dataset show that the proposed strategy increases the long-term cumulative income while the number of workers is within a certain scale.

Key words： Spatial Crowdsourcing Task Allocation Multi-stage Sequential Decision-Making Deep Reinforcement Learning

Received: 18 June 2020

ZTFLH:

TP 18

Fund:National Natural Science Foundation of China(No.91546108, 71901001,71521001), Science and Technology Major Projects of Anhui Province(No.201903a05020020), Natural Science Foundation of Anhui Province(No.1908085QG298)

Corresponding Authors: ZHU Xuhui,Ph.D.,lecturer. His research interests include intelligent computing and machine learning.

About author:: NI Zhiwei, Ph.D., professor. His research interests include artificial intelligence, machine learning and cloud computing.LIU Hao, master student. His research interests include spatial crowdsourcing and reinforcement learning.ZHAO Yang, master student. His research interests include spatial crowdsourcing and intelligent computing.RAN Jiamin, master student. Her research interests include differential privacy and data mining.

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Cite this article:

NI Zhiwei,LIU Hao,ZHU Xuhui等. Task Allocation Strategy of Spatial Crowdsourcing Based on Deep Reinforcement Learning[J]. , 2021, 34(3): 191-205.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202103001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I3/191

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