Efficient exploration in bandit and reinforcement learning
Abstract/Contents
- Abstract
- Sequential decision making problems appear is the core problem in many real world applications. In such problems, an agent is aiming at achieving a certain goal by optimally taking a sequence of actions based on noisy observations. Bandit and reinforcement learning are fundamental frameworks for modeling decision making under uncertainty. Efficient exploration in such problems significantly increases data efficiency by speeding up the learning process and requiring less data for making decisions. As such, it is of utmost importance to design sophisticated exploration schemes based on the special characteristics of each practical problem. In this dissertation, we first consider a safe exploration problem in linear bandits and proposes an algorithm that satisfies safety constraints while minimizing the regret. We provide theoretical analysis and simulation results to demonstrate the efficiency of the proposed algorithm. Then, we consider best arm identification problem in generalized linear bandits and provide a gap-based exploration strategy that achieves desirable accuracy. We also provide an upper bound on the sample complexity of the proposed algorithm and offer numerical studies to evaluate its performance.
Description
| 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 | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Kazerouni, Abbas |
|---|---|
| Degree supervisor | Wein, Lawrence |
| Thesis advisor | Wein, Lawrence |
| Thesis advisor | Van Roy, Benjamin |
| Thesis advisor | Weissman, Tsachy |
| Degree committee member | Van Roy, Benjamin |
| Degree committee member | Weissman, Tsachy |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Abbas Kazerouni. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Abbas Kazerouni
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...