An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms
This addresses the problem of sub-optimal task recommendations in dynamic crowdsourcing environments, though it is incremental as it builds on existing RL methods with specific enhancements.
The paper tackles the task arrangement problem in crowdsourcing platforms by proposing a Deep Reinforcement Learning framework that uses Deep Q-Networks to optimize recommendations for both workers and requesters, achieving superior performance in experiments on synthetic and real datasets.
In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms. Previous works conduct the personalized recommendation of tasks to workers via supervised learning methods. However, the majority of them only consider the benefit of either workers or requesters independently. In addition, they cannot handle the dynamic environment and may produce sub-optimal results. To address these issues, we utilize Deep Q-Network (DQN), an RL-based method combined with a neural network to estimate the expected long-term return of recommending a task. DQN inherently considers the immediate and future reward simultaneously and can be updated in real-time to deal with evolving data and dynamic changes. Furthermore, we design two DQNs that capture the benefit of both workers and requesters and maximize the profit of the platform. To learn value functions in DQN effectively, we also propose novel state representations, carefully design the computation of Q values, and predict transition probabilities and future states. Experiments on synthetic and real datasets demonstrate the superior performance of our framework.