基于多智能体深度强化学习的空间众包任务分配

针对现有空间众包中的任务分配大多只考虑单边、 短期利益和单一场景的问题, 提出一种基于多智能体深度强化学习的空间众包任务分配算法. 首先定义一种新的空间众包场景, 其中工人可以自由选择是否与他人合作； 然后设计基于注意力机制和A2C(advantage actor-critic)方法的多智能体深度强化学习模型进行新场景下的任务分配； 最后进行仿真实验, 并将该算法与其他最新的任务分配算法进行性能对比. 仿真实验结果表明, 该算法能同时实现最高的任务完成率和工人收益率, 证明了该算法的有效性和鲁棒性.

Spatial Crowdsourcing Task Assignment Based on Multi-agent Deep Reinforcement Learning

Aiming at the problem that most of the existing task assignment in spatial crowdsourcing only considered unilateral benefits, short-term benefits and single scenario, we proposed a spatial crowdsourcing task assignment algorithm based on multi-agent deep reinforcement learning. Firstly, a new spatial crowdsourcing scenario was defined, in which workers could freely choose whether to cooperate with others. Secondly, a multi-agent deep reinforcement learning model based on the attention mechanism and A2C (advantage actor-critic) method was designed for task assignment in the new scenario. Finally, simulation experiments were carried out, and the performance of the algorithm was compared with other latest task assignment algorithms. The experimental results show that the proposed algorithm can achieve higher task completion rate and worker profitability rate simultaneously, which proves the effectiveness and robustness of the algorithm.