This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
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
- 4Reinforcement Learning
- 2Real-Time Learning
- 1Alternate policy gradient estimator
- 1Application of Reinforcement Learning
- 1Control
- 1Deep Reinforcement Learning
-
Spring 2022
Policy gradient (PG) estimators are ineffective in dealing with softmax policies that are sub-optimally saturated, which refers to the situation when the policy concentrates its probability mass on sub-optimal actions. Sub-optimal policy saturation may arise from a bad policy initialization or a...
-
Fall 2023
Off-policy policy evaluation has been a critical and challenging problem in reinforcement learning, and Temporal-Difference (TD) learning is one of the most important approaches for addressing it. There has been significant interest in searching for off-policy TD algorithms which find the same...
-
Spring 2023
Vision is one of the essential means for humans to perceive the world. Similarly, today's intelligent robot agents rely on camera images to perform complex tasks in the real world. Due to the ever-changing nature of the real world, intelligent robot agents must continually learn from...
-
Fall 2021
Continuous-time reinforcement learning tasks commonly use discrete time steps of fixed cycle times for actions. Choosing a small action-cycle time in such tasks allows reinforcement learning agents fast reaction and a more temporally detailed perception of the environment. The learning...