This study explores optimization strategies for self-supervised learning (SSL) in the use of unlabeled data. The authors propose a novel method that enhances the performance of SSL algorithms on unlabeled data, achieving improved accuracy and generalization capabilities. The method is validated across multiple datasets, demonstrating superior performance compared to traditional approaches. The paper discusses how to optimize SSL strategies, particularly by combining deep reinforcement learning with generative adversarial networks (GANs). This approach effectively utilizes unlabeled data, improving the generalization and robustness of SSL models. Extensive experiments show that the proposed method performs well on various datasets, highlighting its robust generalization ability. The research is significant for advancing SSL technologies and providing valuable insights for related fields. The introduction highlights the challenges of SSL, such as data distribution imbalances and label noise, and emphasizes the importance of optimization strategies. The core idea of SSL is to use intrinsic data information for learning without manual annotations. The paper discusses various optimization strategies, including data augmentation, model ensembling, and domain adaptation. It also introduces a new method combining deep reinforcement learning and GANs to better utilize unlabeled data. The concept and significance of SSL are explored, detailing its basic principles, advantages, and the optimization strategies involved. The paper delves into the likelihood function optimization process and common optimization strategies, such as contrastive learning and loss functions. It also discusses the challenges and future prospects of SSL, emphasizing the need for continuous exploration and innovation to enhance its effectiveness in practical applications. The conclusion reiterates the importance of SSL in utilizing unlabeled data and the challenges it faces. It highlights the effectiveness of the proposed optimization strategies and the potential for further improvements. The paper concludes by emphasizing the need for interdisciplinary cooperation and sustained efforts to promote the development and application of SSL in various fields.This study explores optimization strategies for self-supervised learning (SSL) in the use of unlabeled data. The authors propose a novel method that enhances the performance of SSL algorithms on unlabeled data, achieving improved accuracy and generalization capabilities. The method is validated across multiple datasets, demonstrating superior performance compared to traditional approaches. The paper discusses how to optimize SSL strategies, particularly by combining deep reinforcement learning with generative adversarial networks (GANs). This approach effectively utilizes unlabeled data, improving the generalization and robustness of SSL models. Extensive experiments show that the proposed method performs well on various datasets, highlighting its robust generalization ability. The research is significant for advancing SSL technologies and providing valuable insights for related fields. The introduction highlights the challenges of SSL, such as data distribution imbalances and label noise, and emphasizes the importance of optimization strategies. The core idea of SSL is to use intrinsic data information for learning without manual annotations. The paper discusses various optimization strategies, including data augmentation, model ensembling, and domain adaptation. It also introduces a new method combining deep reinforcement learning and GANs to better utilize unlabeled data. The concept and significance of SSL are explored, detailing its basic principles, advantages, and the optimization strategies involved. The paper delves into the likelihood function optimization process and common optimization strategies, such as contrastive learning and loss functions. It also discusses the challenges and future prospects of SSL, emphasizing the need for continuous exploration and innovation to enhance its effectiveness in practical applications. The conclusion reiterates the importance of SSL in utilizing unlabeled data and the challenges it faces. It highlights the effectiveness of the proposed optimization strategies and the potential for further improvements. The paper concludes by emphasizing the need for interdisciplinary cooperation and sustained efforts to promote the development and application of SSL in various fields.