[3Win5] Poster session 3

[3Win5-81] Development of a plant operation proposal module using RLHF

〇Akiyuki Seki¹, Masanori Yoshikawa², Shigeru Takaya² (1.Nagano University, 2.Japan Atomic Energy Agency)

Keywords:Reinforcement Learning, Human Feedback, Surrogate Model

A module for proposing plant operation using reinforcement learning has been developed that quickly proposes appropriate operations depending on the state of the plant. However, there was a tendency to propose operations that were difficult to implement in actual operation, such as proposing upper and lower limits for the range of possible operations. In this study, we applied RLHF that incorporates human feedback as a reward and developed a module that can propose more realistic and safer operations.