Advancements in Offline Reinforcement Learning

This chapter explores the nuances of offline reinforcement learning, particularly in power plant applications where exploration is limited and exploitation is prioritized. The discussion includes challenges in transfer learning among distinct plant configurations, advancements in human loop control, and the need for robust algorithms against noisy data. It highlights the differences in AI research approaches between the US and China, emphasizing the importance of developing efficient algorithms for real-world challenges across various sectors.