The field of reinforcement learning is witnessing significant advancements, particularly in offline reinforcement learning and meta-learning. Researchers are exploring innovative methods to improve the efficiency and effectiveness of these approaches. One notable direction is the development of new algorithms that can learn from fixed datasets without requiring further environment interaction. These algorithms have shown promising results in various benchmark tasks, demonstrating their potential for real-world applications. Another area of focus is meta-learning, which enables rapid adaptation to new tasks with minimal data. Studies have evaluated the performance of meta-learning algorithms in robotic manipulation tasks, highlighting their ability to learn universal initializations that facilitate few-shot adaptation. Furthermore, researchers are investigating techniques to improve the transferability of self-supervised learning, addressing task conflict and representation transferability. The integration of transformer-based architectures with deep reinforcement learning is also being explored, aiming to alleviate training difficulties and improve optimal design verification. Overall, these advancements are pushing the boundaries of what is possible in reinforcement learning and meta-learning, with potential applications in areas such as robotics, control, and preference optimization. Noteworthy papers include: Quantile Q-Learning, which proposes a principled method to estimate the temperature coefficient via quantile regression, and Soft Conflict-Resolution Decision Transformer, which introduces a dynamic sparsity adjustment strategy to mitigate gradient conflicts. Additionally, the paper on Evaluating Model-Agnostic Meta-Learning on MetaWorld ML10 Benchmark demonstrates the effectiveness of meta-learning in robotic manipulation tasks, while the paper on Transformer-Guided Deep Reinforcement Learning showcases the potential of transformer-based architectures in optimal takeoff trajectory design for eVTOL drones.