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Spring 2021
On July 20, 1969, the Apollo 11 lunar module, with Astronauts Neil Armstrong and Buzz Aldrin aboard, landed on the moon. It was a great achievement in space exploration. Most people know of this mission's success; yet, there is an untold story about this mission that many people are not aware...
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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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Spring 2020
Reinforcement learning (RL) has received wide attention in various fields lately. Model-free RL brings data-driven solutions that learn the control strategy directly from interaction with process data without the need for a process model. This is especially beneficial in the case of nonlinear...
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Spring 2018
Recent advancements in reinforcement learning have made the field interesting to academia and industry alike. Many of these advancements depend on deep learning as a means to approximate a value function or a policy. This dependency usually relies on high performance hardware (e.g., a graphics...
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Reinforcement Learning-Driven Local Transactive Energy Market for Distributed Energy Resources
DownloadFall 2023
Technological breakthroughs in renewable power generation, battery storage, electric mobility, and advanced data logistics are changing the electric grid. The huge influx of distributed energy resources (DERs), while important to curb carbon emissions, is not without consequences. The highly...
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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 2022
While traditional machine learning algorithms learn to solve a task directly, meta- learning aims to learn about and improve another learning algorithm’s performance. However, existing meta-learning methods either only work with differentiable algo- rithms or are handcrafted to improve a specific...
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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...