Task Allocation Strategy of Spatial Crowdsourcing Based on Deep Reinforcement Learning
NI Zhiwei1,2, LIU Hao1,2, ZHU Xuhui1,2, ZHAO Yang1,2, RAN Jiamin1,2
1. School of Management, Hefei University of Technology, Hefei 230009 2. Key Laboratory of Process Optimization and Intelligent Deci-sion-Making, Ministry of Education, Hefei University of Tech-nology, Hefei 230009
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.
倪志伟, 刘浩, 朱旭辉, 赵杨, 冉家敏. 基于深度强化学习的空间众包任务分配策略[J]. 模式识别与人工智能, 2021, 34(3): 191-205.
NI Zhiwei, LIU Hao, ZHU Xuhui, ZHAO Yang, RAN Jiamin. Task Allocation Strategy of Spatial Crowdsourcing Based on Deep Reinforcement Learning. , 2021, 34(3): 191-205.
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