  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.
