Reinforcement Learning for Limited Labeled Classification
Xaolin Chun · 2023
This paper explores the application of reinforcement learning techniques in the context of limited labeled classification tasks. Limited labeled data scenarios, where the availability of labeled samples is scarce or expensive, pose significant challenges for traditional supervised learning methods. Leveraging the power of reinforcement learning, this study proposes a novel framework that addresses the issue of limited labeled data by combining active exploration and uncertainty-based strategies. The framework utilizes an agent that interacts with the environment, actively selecting instances to label based on uncertainty estimates and exploration-exploitation trade-offs. Reinforcement learning algorithms, such as Q-learning or policy gradient methods, are employed to train the agent to make informed decisions on the most informative instances to label. Experimental evaluations on various benchmark datasets demonstrate the effectiveness of the proposed approach, showcasing improved classification performance compared to traditional supervised learning methods in limited labeled scenarios. The findings of this study provide insights into the potential of reinforcement learning for addressing the challenges posed by limited labeled classification tasks, offering a promising avenue for practical applications in domains with limited labeled data availability.