Topics in convex optimization for optimal control and reinforcement learning

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Optimal control describes the problem of finding a control to minimize an objective function for a dynamical system over a period of time. It has been applied in a wide variety of applications in robotics, signal processing, and machine learning over several decades. On the other hand, reinforcement learning (RL) assumes the uncertain model of transition dynamics and has achieved great empirical success in recent years including simulated game scenarios and robotics. This thesis focuses on topics in both optimal control and reinforcement learning.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2021; ©2021
Publication date 2021; 2021
Issuance monographic
Language English


Author Kim, Jong Ho
Degree supervisor Boyd, Stephen P
Thesis advisor Boyd, Stephen P
Thesis advisor Lall, Sanjay
Thesis advisor Pilanci, Mert
Degree committee member Lall, Sanjay
Degree committee member Pilanci, Mert
Associated with Stanford University, Department of Electrical Engineering


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Jongho Kim.
Note Submitted to the Department of Electrical Engineering.
Thesis Thesis Ph.D. Stanford University 2021.

Access conditions

© 2021 by Jong Ho Kim
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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