Explore an innovative approach to Model Predictive Control (MPC) through this 47-minute conference talk presented by Byron Boots from the Georgia Institute of Technology. Delivered at the ADSI Summer Workshop on Algorithmic Foundations of Learning and Control, hosted by the Paul G. Allen School of Computer Science & Engineering at the University of Washington, the presentation delves into the integration of online learning techniques with MPC. Gain insights into how this novel approach can enhance the performance and adaptability of control systems in various applications. Understand the theoretical foundations and practical implications of combining machine learning algorithms with traditional control methods to address complex, dynamic environments.
An Online Learning Approach to Model Predictive Control - 2019 ADSI Summer Workshop
Paul G. Allen School via YouTube
Overview
Syllabus
2019 ADSI Summer Workshop: Algorithmic Foundations of Learning and Control, Byron Boots
Taught by
Paul G. Allen School
Related Courses
-
Linear Quadratic Control and Online Learning - 2019 ADSI Summer Workshop
-
Reinforcement Learning From Small Data in Feature Space - 2019 ADSI Summer Workshop
-
Non-Asymptotic Analysis of a Classical System Identification Algorithm - 2019 ADSI Summer Workshop
-
Sample-Efficient Exploration in Reinforcement Learning with Rich Observations - 2019
-
2019 ADSI Summer Workshop- Algorithmic Foundations of Learning and Control
-
2019 ADSI Summer Workshop- Algorithmic Foundations of Learning and Control
