Search
Skip to Search Results- 35Reinforcement learning
- 5Machine learning
- 3Artificial Intelligence
- 3Non-player character
- 2Adaptive switching
- 2Intelligent agents
- 2Cutumisu, Maria
- 1Al-Saffar, Mohammed
- 1Atrazhev, Peter
- 1Bastani, Meysam
- 1Bowling, Michael
- 1Carbonaro, Mike
- 30Graduate and Postdoctoral Studies (GPS), Faculty of
- 30Graduate 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)
-
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...
-
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...
-
Spring 2024
The increasing demand for electricity driven by the widespread adoption of electric vehicles necessitates effective distribution network reconfiguration methods. However, existing distribution network reconfiguration approaches often rely on precise network parameters, leading to scalability and...
-
Spring 2024
Machine-learning techniques are emerging as a valuable tool in experimental physics, and among them, reinforcement learning offers the potential to control high-dimensional, multistage processes in the presence of fluctuating environments. In this experimental work, we apply reinforcement...
-
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...
-
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...
-
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...
-
Spring 2024
In reinforcement learning, the notion of state plays a central role. A reinforcement learning agent requires the state to evaluate its current situation, select actions, and construct a model of the environment. In the classic setting, it is assumed that the environment provides the agent with...
-
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...