Robust learning and evaluation in sequential decision making
Abstract/Contents
- Abstract
- Reinforcement learning (RL), as a branch of artificial intelligence, is concerned with making a good sequence of decisions given experience and rewards in a stochastic environment. RL algorithms, propelled by the rise of deep learning and neural networks, have shown an impressive performance in achieving human-level performance in games like Go, Chess, and Atari. However, when applied to high-stakes real-world applications, these impressive performances are not matched. This dissertation tackles some important challenges around robustness that hinder our ability to unleash the potential of RL to real-world applications. We look at the robustness of RL algorithms in both online and offline settings. In an online setting, we develop an algorithm for sample efficient safe policy learning. In an offline setting, we tackle issues of unobserved confounders and heterogeneity in off-policy policy evaluation.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Keramati, Ramtin |
|---|---|
| Degree supervisor | Brunskill, Emma |
| Thesis advisor | Brunskill, Emma |
| Thesis advisor | Pavone, Marco, 1980- |
| Thesis advisor | Van Roy, Benjamin |
| Degree committee member | Pavone, Marco, 1980- |
| Degree committee member | Van Roy, Benjamin |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ramtin Keramati. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/dd732zb2339 |
Access conditions
- Copyright
- © 2021 by Ramtin Keramati
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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