<p>Multi-view learning has been capturing widespread attention from a variety of areas, while very limited labeling information poses a great challenge for multi-view semi-supervised classification. Especially, rare labels often require multi-hop label propagation, which leads to over-smoothing and inaccurate prediction. In contrast, reinforcement learning can capture long-term policy series by a reward function. In this paper, we address the semi-supervised multi-view classification problem from a reinforcement learning perspective. The proposed approach frames the classification problem as a process where an agent, guided by reinforcement learning, starts from the state representing the unlabeled sample, transitions to the state corresponding to the label, and completes the label assignment. The reward matrix’s core is defined as the affinity matrix between samples, complemented by one-hot encoding in the label space and an identity matrix. The Q-table is then derived from the rewards in Q-learning, enabling the use of limited label information to effectively mine multi-view data. In the label assignment phase, the agent utilizes the table to eventually reach the state corresponding to the label and assign that label to the sample represented by the initial state. Our method’s effectiveness is confirmed through extensive experiments on multi-view semi-supervised classification tasks compared with several advanced approaches.</p>