The recent developments in the field of reinforcement learning (RL) have shown a significant shift towards addressing complex and real-world challenges. A notable trend is the focus on offline RL, where methods are being developed to handle out-of-distribution (OOD) states and actions, enhancing the robustness and applicability of RL agents in diverse environments. Innovations such as value-aware OOD state correction and action suppression are proving effective in improving agent performance without the need for additional hyperparameter tuning. Additionally, there is a growing interest in in-context learning (ICL) under random policies, which aims to generalize RL to new tasks without optimal policy requirements, making it more feasible for real-world applications. The introduction of strategic planning approaches for zero-shot in-context learning is also advancing the field by addressing error accumulation in diverse task scenarios. Furthermore, the development of efficient experience replay techniques, particularly those leveraging diversity in state realizations, is enhancing learning efficiency in sparse reward environments. Lastly, advancements in handling combinatorial action spaces in offline RL are providing scalable solutions to complex decision-making problems. These developments collectively push the boundaries of RL applicability and robustness, making it a promising area for future research and practical implementations.