Title: Reinforced Sample Reweighting Policy for Semi-supervised Learning. Abstract: Semi-supervised learning (SSL) has been shown to be an effective paradigm for learning with less labeled data. To improve the performance of SSL, existing methods build sample reweighting or thresholding strategies to handle the category bias or erroneous pseudo labels. However, most of these existing methods are based on the heuristic hand-crafted rules, which require laborious adjustment, and may lead to sub-optimal solutions that cannot improve the model performance to the greatest extent. Here, to the best of our knowledge, we pioneer to develop an automatic strategy that boosts the performance of SSL. We introduce an end-to-end sample reweighting policy for semi-supervised learning, with a delicately designed Markov Decision Process (MDP) framework. The MDP framework is constructed with an agent network, which is optimized in a reward-driven manner, and receives the carefully designed state and action representations for decision reference. We also design a memory paradigm for computation-efficient representation construction and MDP solving. We further introduce a "pretraining-boosting" two-stage MDP curriculum where the agent network is firstly pretrained and then optimized continuously in the deployment phase to catch up with the constantly updated classification network. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple datasets, outperforming previous advanced approaches such as FixMatch.