Allocation of English Distance Teaching Resources based on Deep Reinforcement Learning and Multi-Objective Optimization

Abstract

This work employs deep reinforcement learning and multi-objective optimization algorithms to the allocation of English distance teaching resources in order to increase their allocation efficiency. Moreover, based on the analysis of current regression correction, this paper discusses the algorithm of partition regression correction in depth, and proposes two different neighborhood regression correction algorithms. The proposal of neighborhood further expands the original concept of partition and solves various problems in partition correction. In order to reduce the model complexity of the neighborhood regression algorithm, this paper proposes to solve the problem through structural risk minimization and principal component extraction. The simulation results suggest that the English distance teaching resource allocation approach described in this research, which is based on deep reinforcement learning and multi-objective optimization, may significantly enhance the English distance teaching resource allocation impact.