Offline reinforcement learning (RL) aims to train policies using previously collected data without additional online interaction. This approach is promising for turning large datasets into powerful decision-making systems, applicable in domains like healthcare, education, and robotics. However, current algorithms face significant challenges, particularly in handling distributional shift and ensuring effective learning from offline data. The paper reviews key concepts, challenges, and recent solutions in offline RL, emphasizing the need for methods that can generalize well from limited data. It discusses the differences between online and offline RL, highlighting the limitations of traditional methods and the potential of data-driven approaches. The paper also explores various RL algorithms, including policy gradients, approximate dynamic programming, and actor-critic methods, and their adaptations for offline settings. It addresses the difficulties of offline RL, such as the inability to explore new states and the risk of distributional shift, and proposes strategies to mitigate these issues. The paper concludes with a discussion of future research directions and open problems in the field.Offline reinforcement learning (RL) aims to train policies using previously collected data without additional online interaction. This approach is promising for turning large datasets into powerful decision-making systems, applicable in domains like healthcare, education, and robotics. However, current algorithms face significant challenges, particularly in handling distributional shift and ensuring effective learning from offline data. The paper reviews key concepts, challenges, and recent solutions in offline RL, emphasizing the need for methods that can generalize well from limited data. It discusses the differences between online and offline RL, highlighting the limitations of traditional methods and the potential of data-driven approaches. The paper also explores various RL algorithms, including policy gradients, approximate dynamic programming, and actor-critic methods, and their adaptations for offline settings. It addresses the difficulties of offline RL, such as the inability to explore new states and the risk of distributional shift, and proposes strategies to mitigate these issues. The paper concludes with a discussion of future research directions and open problems in the field.