Model-based Reinforcement Learning for Control Problems

Established: August 1, 2017

This research project aims at developing a new class of Reinforcement Learning (RL) algorithms that are sample efficient, off policy, and transferable. We seek to demonstrate these new algorithms in real-world operational optimal control applications such as

Indoor Farm Control