Deep Reinforcement Learning based Optimal Control of Complex Systems
This team investigates how to utilize deep reinforcement learning for optimal control of complex systems.
Guang Lin, Associate Professor of Mathematics, School of Mechanical Engineering, email@example.com
Machine learning discovers statistical knowledge from data and has escaped from the cage of perception. A growing number of complex systems from walking robots, drones to the computer Go player rely on learning techniques to make decisions to achieve optimal control of complex systems. This change represents a truly fundamental departure from traditional classification and regression methods as such learning systems must deal with sequential multi-stage decision making and long-period control horizons and the trade-off between exploration and exploitation. This team investigates how to utilize deep reinforcement learning for optimal control of complex systems.
This project includes both theory and implementation in software. Students will learn the concepts and applications of transfer learning.
The members are expected to have finished one semester of calculus and one programming course.
- Wednesdays 4:30 - 5:20 pm (Fall 2019)
- Wednesdays 1:30 - 2:20 pm in EE 013 (Spring 2020)