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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
- 1Approximate policy iteration,
- 1Approximate value iteration
- 1Bandits
- 1Computer Vision
- 1Deep Learning
- 1Empirical Game Theory
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Improving Different Aspects in RL - Accelerating Convergence Rate & Enhancing Safety and Robustness
DownloadFall 2021
Reinforcement learning (RL) has moved from toy domains to real-world applications, while each of these applications has inherent difficulties which are long-standing challenges in RL, such as: stucking at plateaus, limited training time, costly exploration and safety considerations. I, with my...
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Learning Deep Representations, Embeddings and Codes from the Pixel Level of Natural and Medical Images
DownloadFall 2013
Significant research has gone into engineering representations that can identify high-level semantic structure in images, such as objects, people, events and scenes. Recently there has been a shift towards learning representations of images either on top of dense features or directly from the...
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Spring 2016
In model-based reinforcement learning a model is learned which is then used to find good actions. What model to learn? We investigate these questions in the context of two different approaches to model-based reinforcement learning. We also investigate how one should learn and plan when the reward...
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Fall 2016
In an online learning problem a player makes decisions in a sequential manner. In each round, the player receives some reward that depends on his action and an outcome generated by the environment while some feedback information about the outcome is revealed. The goal of the player can be...