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An Embedding-based Deterministic Policy Gradient Model for Spatial Crowdsourcing Applications | IEEE Conference Publication | IEEE Xplore

An Embedding-based Deterministic Policy Gradient Model for Spatial Crowdsourcing Applications


Abstract:

Most spatial crowdsourcing systems are designed in a static mode with tasks allocated based on the historical interactions data between crowd participants and crowdsourci...Show More

Abstract:

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.
Date of Conference: 05-07 May 2021
Date Added to IEEE Xplore: 28 May 2021
ISBN Information:
Conference Location: Dalian, China

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