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- 23Machine Learning
- 12Artificial Intelligence
- 6Transfer Learning
- 5Planning
- 5Representation Learning
- 91Graduate and Postdoctoral Studies (GPS), Faculty of
- 91Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 5Computing Science, Department of
- 5Computing Science, Department of/Technical Reports (Computing Science)
- 3WISEST Summer Research Program
- 3WISEST Summer Research Program/WISEST Research Posters
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Spring 2019
In the reinforcement learning (RL) problem an agent must learn how to act optimally through trial-and-error interactions with a complex, unknown, stochastic environment. The actions taken by the agent influence not just the immediate reward it observes but also the future states and rewards it...
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Fall 2024
This thesis studies a virtual power plant (VPP) that trades the bidirectional charging flexibility of privately owned plug-in electric vehicles (EVs) in a real-time electricity market to maximize its profit. The main contribution of this thesis is the development of scalable and efficient...
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Spring 2011
Off-policy reinforcement learning is useful in many contexts. Maei, Sutton, Szepesvari, and others, have recently introduced a new class of algorithms, the most advanced of which is GQ(lambda), for off-policy reinforcement learning. These algorithms are the first stable methods for general...
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Fall 2022
OpenSpiel is an open-source software system for implementing high-performance software players for many different computer games. Hex is a two-player game of perfect information used in a variety of computer games research projects. The OpenSpiel project has implemented a version of the AlphaZero...
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Feature Generalization in Deep Reinforcement Learning: An Investigation into Representation Properties
DownloadFall 2022
In this thesis, we investigate the connection between the properties and the generalization performance of representations learned by deep reinforcement learning algorithms. Much of the earlier work on representation learning for reinforcement learning focused on designing fixed-basis...
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2007
Technical report TR07-12. One key topic in reinforcement learning is function approximation which is critical for the success of reinforcement learning in domains with large state spaces. Unfortunately, function approximation can lead to several problems including the suboptimality of the...
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Spring 2010
This research focuses on developing AI agents that play arbitrary Atari 2600 console games without having any game-specific assumptions or prior knowledge. Two main approaches are considered: reinforcement learning based methods and search based methods. The RL-based methods use feature vectors...
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Fall 2022
This thesis investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives,...
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Fall 2011
We present a new family of gradient temporal-difference (TD) learning methods with function approximation whose complexity, both in terms of memory and per-time-step computation, scales linearly with the number of learning parameters. TD methods are powerful prediction techniques, and with...