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A task allocation algorithm based on reinforcement learning in spatio-temporal crowdsourcing
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DOI：10.1007/s10489-022-04151-6.png)
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Abstract
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With the pervasiveness of dynamic task allocation in sharing economy applications, online bipartite graph matching has attracted more and more research attention. In sharing economy applications, crowdsourcing platforms need to allocate tasks to workers dynamically. Previous studies have low allocation utility. To increase the allocation utility of the Spatio-temporal crowdsourcing system, this paper proposes a dynamic delay bipartite matching(DDBM) problem, and designs Value Based Task Allocation(VBTA) and Policy Gradient Based Task Allocation(PGTA) frameworks respectively. According to the current state, VBTA and PGTA could enhance the allocation utility by selecting appropriate thresholds. The convergence of the algorithm is proved. Extensive experimental results on two real datasets demonstrate that the proposed algorithms are superior to the existing algorithms in effectiveness and efficiency.
Keywords:
Task allocation
Spatio-temporal crowdsourcing
Markov model
Q-learning
Policy gradient
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