Model-based Reinforcement Learning for Control Problems

Model-based Reinforcement Learning for Control Problems

Established: August 1, 2017

Overview

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

Greenhouse   

Data Center Energy Consumption Optimization

Data center

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