基于深度强化学习的建筑学专业基础类课程教学资源分配方法

Teaching Resource Allocation Method for Basic Courses of Architecture Major Based on Deep Reinforcement Learning

传统建筑学专业基础类课程教学资源分配方法对下降梯度的计算不够精细,导致教学资源分配结果的召回率不高,为此提出基于深度强化学习的建筑学专业基础类课程教学资源分配方法。首先,确定资源分配关键节点,基于深度强化学习计算分配梯度;其次,基于深度强化学习编写资源分配方法,输出教学资源分配结果;最后,设计对比实验。实验结果表明,该方法的召回率高于其他方法,能够有效提高教学资源分配结果的召回率。

The traditional allocation method of teaching resources for basic courses in architecture is not precise enough in calculating the descent gradient,resulting in a low recall rate of teaching resource allocation results.Therefore,a deep reinforcement learning based teaching resource allocation method for basic courses in architecture is proposed.Firstly,determine the key nodes for resource allocation and calculate the allocation gradient based on deep reinforcement learning.Secondly,based on deep reinforcement learning,write resource allocation methods and output teaching resource allocation results.Finally,design a comparative experiment.The experimental results show that the average recall rate of this method is 0.247,which is superior to other methods and can effectively improve the recall rate of teaching resource allocation results.