Weisz, Gellért;
(2024)
The Complexity of Reinforcement Learning with Linear Function Approximation.
Doctoral thesis (Ph.D), UCL (Univesity College London).
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Abstract
In this thesis we present contributions to the theoretical foundations of large-scale reinforcement learning (RL) with linear function approximation, with a focus on establishing classes of problems that are theoretically solvable in polynomial time and ones that are not. The problem classes differ in learning paradigm and structural assumptions. We start with the problem of planning under
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | The Complexity of Reinforcement Learning with Linear Function Approximation |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10186627 |
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