From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have an important impact. That may feel especially true, for example, when you're a passenger late for dinner in an autonomous car that has learned the efficient way home. Work in Jr-Shin Li's lab develops mathematically rigorous and computationally efficient techniques to transform extremely complex reinforcement learning problems into a manageable domain.
Why reinforcement learning breaks at scale, and how a new method fixes it
