On June 2nd, Dr. Ning Lin, MACH Co-PI and Professor of Civil and Environmental Engineering at Princeton University, joined the PNAS (Proceedings of the National Academy of Sciences) podcast Science Sessions to discuss recent research on reinforcement learning methods.
“Reinforcement learning is an area of machine learning regarding specifically how agents or decision makers will make decision[s] or act in changing environmental states in order to maximize their cumulative rewards. So, in this study, we investigate this potential and performance of reinforcement learning when applied to adaptive climate decision making. Specifically, we apply reinforcement learning to design coastal flood protection strategies that evolve over time according to future observations of sea level rise with a goal of minimizing the total cost, including the investment and the damage over time.”
Read the transcript or listen to the episode online here.
Or visit this link if you are interested in reading the published paper “Reinforcement learning–based adaptive strategies for climate change adaptation: An application for coastal flood risk management” by Kairui Feng, Ning Lin, Robert Kopp, and Michael Oppenheimer.