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Theses and Dissertations
This collection contains theses and dissertations of graduate students of the University of Alberta. The collection contains a very large number of theses electronically available that were granted from 1947 to 2009, 90% of theses granted from 2009-2014, and 100% of theses granted from April 2014 to the present (as long as the theses are not under temporary embargo by agreement with the Faculty of Graduate and Postdoctoral Studies). IMPORTANT NOTE: To conduct a comprehensive search of all UofA theses granted and in University of Alberta Libraries collections, search the library catalogue at www.library.ualberta.ca - you may search by Author, Title, Keyword, or search by Department.
To retrieve all theses and dissertations associated with a specific department from the library catalogue, choose 'Advanced' and keyword search "university of alberta dept of english" OR "university of alberta department of english" (for example). Past graduates who wish to have their thesis or dissertation added to this collection can contact us at erahelp@ualberta.ca.
Items in this Collection
- 3Reinforcement Learning
- 1Actor-Expert
- 1Continuous action space
- 1Control
- 1Linear Function Approximation
- 1Nonlinear Function Approximation
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Fall 2019
Q-learning can be difficult to use in continuous action spaces, because a difficult optimization has to be solved to find the maximal action. Some common strategies have been to discretize the action space, solve the maximization with a powerful optimizer at each step, restrict the functional...
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Fall 2019
In this thesis, we investigate sparse representations in reinforcement learning. We begin by discussing catastrophic interference in reinforcement learning with function approximation, and empirically investigating difficulties of online reinforcement learning in both policy evaluation and...
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Fall 2019
Policy evaluation, learning value functions, is an integral part of the reinforcement learning problem. In this thesis, I propose a neural network architecture, the Two-Timescale Network (TTN), for value function approximation which utilizes linear function approximation for the value function...