An Embedding-based Deterministic Policy Gradient Model for Spatial Crowdsourcing Applications
PROCEEDINGS OF THE 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD)(2021)
摘要
Most spatial crowdsourcing systems are designed in a static mode with tasks allocated based on the historical interactions data between crowd participants and crowdsourcing tasks. However, these task assignment algorithms usually ignore the long-term feedback on interactive spatial crowdsourcing systems, resulting in performance degradation. Though reinforcement learning naturally fits the problem of maximizing long term crowdsourcing rewards, deep reinforcement learning-based task assignment is still facing the challenge of interactive spatial crowdsourcing. To address these issues, this paper investigates a challenge problem which we study how to intelligently task assignments for interactive spatial crowdsourcing applications. Therefore, we develop an advanced Embedding-based Deterministic Policy Gradient learning framework to maximize long term crowdsourcing rewards for task assignments, called EDPG-Assignment. EDPG-Assignment is based on deep actor critic learning and combines the improvements of two advanced methods, action embedding and neighbor-based deep Deterministic Policy Gradient, and employed this to optimize the task assignment in interactive crowdsourcing. A matrix factorization method to learn spatial crowdsourcing action embedding for neighbor-based discrete actions similarities evaluation in deep actor critic learning-based task assignment from generated crowdsourcing trajectories without any prior knowledge. The EDPG-Assignment algorithm provided a more stable learning process and showed improved results in real-world dataset.
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关键词
task assignment algorithms,interactive spatial crowdsourcing systems,deep actor critic learning,spatial crowdsourcing action,EDPG-assignment algorithm,deep reinforcement learning,neighbor based discrete actions similarities evaluation,task allocation,embedding based deterministic policy gradient learning,matrix factorization
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