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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
- 23reinforcement learning
- 4machine learning
- 3artificial intelligence
- 3planning
- 2continual learning
- 2deep learning
- 1Bennett, Brendan
- 1Carvalho, Tales Henrique
- 1Chan, Alan
- 1De Asis, Kris
- 1Guo, Yourui
- 1Holland, Gordon Z.
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Fall 2018
Temporal-difference (TD) learning is an important approach for predictive knowledge representation and sequential decision making. Within TD learning exists multi-step methods which unify one-step TD learning and Monte Carlo methods in a way where intermediate algorithms can outperform either...
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Advances in Distributional Reinforcement Learning: Bridging Theory with Algorithmic Practice
DownloadFall 2024
This thesis comprehensively investigates Distributional Reinforcement Learning~(RL), a vibrant research field that interplays between statistics and RL. As an extension of classical RL, distributional RL, on the one hand, embraces plenty of statistical ideas by incorporating distributional...
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Fall 2022
In most, if not every, realistic sequential decision-making tasks, the decision-making agent is not able to model the full complexity of the world. In reinforcement learning, the environment is often much larger and more complex than the agent, a setting also known as partial observability. In...
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Fall 2021
Reinforcement learning (RL) is a learning paradigm focusing on how agents interact with an environment to maximize cumulative reward signals emitted from the environment. Exploration versus exploitation challenge is critical in RL research: the agent ought to trade off between taking the known...
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Fall 2022
Imperfect information games model many large-scale real-world problems. Hex is the classic two-player zero-sum no-draw connection game where each player wants to join their two sides. Dark Hex is an imperfect information version of Hex in which each player sees only their own moves. Finding Nash...
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Ensembling Diverse Policies Improves Generalization of Deep Reinforcement Learning Algorithms to Environmental Changes in Continuous Control Tasks
DownloadFall 2023
Deep Reinforcement Learning (DRL) algorithms have shown great success in solving continuous control tasks. However, they often struggle to generalize to changes in the environment. Although retraining may help policies adapt to changes, it may be quite costly in some environments. Ensemble...
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Spring 2021
Temporal difference (TD) methods provide a powerful means of learning to make predictions in an online, model-free, and highly scalable manner. In the reinforcement learning (RL) framework, we formalize these prediction targets in terms of a (possibly discounted) sum of rewards, called the...
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Spring 2024
Searching for programmatic policies to solve a reinforcement learning problem can be challenging, particularly when dealing with domain-specific languages (DSLs) that define policies with internal states for partially observable Markov decision processes (POMDPs). This is because they lead to...
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Fall 2024
Despite the brain's inherent ability to continually learn, biological insights are rarely applied to continual reinforcement learning (RL). This thesis addresses this gap by examining four under-investigated biologically-inspired modifications within the context of continual RL: energy...
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Fall 2024
Value-based reinforcement learning is an approach to sequential decision making in which decisions are informed by learned, long-horizon predictions of future reward. This dissertation aims to understand issues that value-based methods face and develop algorithmic ideas to address these issues....