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Skip to Search Results- 30Reinforcement learning
- 5Active learning
- 5Machine learning
- 3Artificial Intelligence
- 3Non-player character
- 2Adaptive switching
- 2Cutumisu, Maria
- 1Al-Saffar, Mohammed
- 1Atrazhev, Peter
- 1Bastani, Meysam
- 1Bowling, Michael
- 1Campbell, Sandy
- 28Graduate and Postdoctoral Studies (GPS), Faculty of
- 28Graduate and Postdoctoral Studies (GPS), Faculty of /Theses and Dissertations
- 3Computing Science, Department of
- 3Computing Science, Department of/Technical Reports (Computing Science)
- 1Electrical and Computer Engineering, Department of
- 1Electrical and Computer Engineering, Department of/Journal Articles (Electrical and Computer Engineering)
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Spring 2013
Many machine learning algorithms learn from the data by capturing certain interesting characteristics. Decision trees are used in many classification tasks. In some circumstances, we only want to consider fixed-depth trees. Unfortunately, finding the optimal depth-d decision tree can require time...
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Fall 2009
Learning and planning are two fundamental problems in artificial intelligence. The learning problem can be tackled by reinforcement learning methods, such as temporal-difference learning, which update a value function from real experience, and use function approximation to generalise across...
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Reinforcement Learning based Distributed BESS Management for Mitigating Overvoltage Issues in Systems with High PV Penetration
Download2020-01-01
Al-Saffar, Mohammed, Musilek, Petr
High levels of penetration of distributed photovoltaic generators can cause serious overvoltage issues, especially during periods of high power generation and light loads. There have been many solutions proposed to mitigate the voltage problems, some of them using battery energy storage systems...
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Spring 2022
The repetitiveness and precision of manufacturing tasks has increased the need for robots in the automation of the manufacturing industry; however, the complex and varied nature of manufacturing production lines poses challenges in terms of applying the rule-based automation approach. This has...
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Spring 2023
Process industries involve processes that have complex, interdependent, and sometimes uncontrollable/unobservable features that are subject to a variety of uncertainties such as operational fluctuations, sensory noises, process anomalies, human involvement, market volatility, and so forth. In the...
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Fall 2015
This dissertation first introduces the concepts of robust active learning (also called optimal experimental design in statistics), and the possible advantages of it over the traditional passive learning method. Then a general regression problem with possibly misspecified models is presented, and...
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2007
Wang, Tao, Schuurmans, Dale, Bowling, Michael, Lizotte, Daniel
Technical report TR07-05. We investigate novel, dual algorithms for dynamic programming and reinforcement learning, based on maintaining explicit representations of stationary distributions instead of value functions. In particular, we investigate the convergence properties of standard dynamic...
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Spring 2012
We study linear estimation based on perturbed data when performance is measured by a matrix norm of the expected residual error, in particular, the case in which there are many unknowns, but the “best” estimator is sparse, or has small L1-norm. We propose a Lasso-like procedure that finds the...
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Fall 2017
The idea of an amputee playing the piano with all the flair and grace of an able-handed person may seem like a futuristic fantasy. While many prosthetic limbs look lifelike, finding one that also moves naturally has proved more of a challenge for both researchers and amputees. Even though...