Offline Rl

Offline Reinforcement Learning (Offline RL) is a method of reinforcement learning that trains on a fixed dataset without the need for real-time interaction with the environment. Its goal is to optimize decision-making policies by leveraging historical data, thereby improving the model's performance in new environments. Offline RL has significant application value in areas such as gaming, recommendation systems, and autonomous driving, effectively addressing issues of high data collection costs and low safety associated with online learning.