论文标题
加强分配问题的学习
Reinforcement Learning for Assignment problem
论文作者
论文摘要
本文致力于将增强学习与神经网络结合使用用户调度问题的一般表述。我们的模拟器通过环境的随机变化类似于现实世界中的问题。我们将基于Q学习的方法应用于动态模拟的数量，并且在总奖励方面优于基于分析的贪婪解决方案，其目的是在整个模拟过程中获得最低的惩罚。
This paper is dedicated to the application of reinforcement learning combined with neural networks to the general formulation of user scheduling problem. Our simulator resembles real world problems by means of stochastic changes in environment. We applied Q-learning based method to the number of dynamic simulations and outperformed analytical greedy-based solution in terms of total reward, the aim of which is to get the lowest possible penalty throughout simulation.
