Using Data Augmentation to Improve Sample Efficiency

Nicklas Hansen is a Ph.D. student at UC San Diego advised by Prof Xiaolong Wang and Prof Hao Su. He is also a student researcher at Meta AI. Nicklas' research interests involve developing machine learning systems, specifically neural agents, that have the ability to learn, generalize, and adapt over their lifetime. In this episode, we talk about long-horizon planning, adapting reinforcement learning policies during deployment, why algorithms don't drive research progress, and much more!