In recent years, semi-supervised learning (SSL) has gained significant
attention due to its ability to leverage both labeled and unlabeled data to
improve model performance, especially when labeled data is scarce. However,
most current SSL methods rely on heuristics or predefined rules for generating
pseudo-labels and leveraging unlabeled data. They are limited to exploiting
loss functions and regularization methods within the standard norm. In this
paper, we propose a novel Reinforcement Learning (RL) Guided SSL method,
RLGSSL, that formulates SSL as a one-armed bandit problem and deploys an
innovative RL loss based on weighted reward to adaptively guide the learning
process of the prediction model. RLGSSL incorporates a carefully designed
reward function that balances the use of labeled and unlabeled data to enhance
generalization performance. A semi-supervised teacher-student framework is
further deployed to increase the learning stability. We demonstrate the
effectiveness of RLGSSL through extensive experiments on several benchmark
datasets and show that our approach achieves consistent superior performance
compared to state-of-the-art SSL methods.

提出了一种新颖的强化学习引导的半监督学习方法 RLGSSL，将半监督学习视为一个老虎机问题，并通过加权奖励的创新 RL 损失来自适应地指导预测模型的学习过程，通过在多个基准数据集上进行广泛实验表明，相较于最先进的半监督学习方法，我们的方法始终具有更好的性能。