  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.
