Transfer Reinforcement Learning

Transfer Reinforcement Learning is a method of reinforcement learning that accelerates the learning process for new tasks by leveraging knowledge acquired from previous tasks. Its core objective is to achieve knowledge transfer between different but related tasks, thereby enhancing learning efficiency and performance. This approach has significant application value in multi-task environments, continual learning, and adaptive systems, as it can significantly reduce training time and resource consumption while improving the system's generalization and adaptability.