Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"Optimal Control; Reinforcement Learning"”.
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Efficient Reinforcement Learning for Control
The landscape of control systems has evolved rapidly with the emergence of Reinforcement Learning (RL), offering promising solutions to a wide range of dynamic decision-making problems. However, the application of RL to real-world control systems is often hindered by computational inefficiencies, …
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An Introduction to Reinforcement Learning
This thesis presents a new course textbook on reinforcement learning (RL) with a focus on algorithms and their properties. The textbook is suitable for a one-semester introductory undergraduate course on RL for students with prior experience in basic probability, linear algebra, and multivariable …
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Modeling, predicting, and guiding users' temporal behaviors
… a novel probabilistic framework for modeling, learning, predicting, and guiding users’ temporal behaviors. Within the proposed framework, we introduce a pipeline of newly developed statistical models, state-of-the-arts learning algorithms to tackle several canonical problems in theory and …
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Nonlinear Energy Harvesting With Tools From Machine Learning
… is close to its natural frequency. While various control methods applied to an energy harvester realize resonant frequency tuning, they are either energy-consuming or exhibit low efficiency when operating under multi-frequency excitations. In order to overcome these limitations in a linear energy …
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The Science of Mind Reading: New Inverse Optimal Control Framework
Continuous control and planning by the brain remain poorly understood and is a major challenge in the field of Neuroscience. To truly say that we understand the underlying mechanisms we should first be able to explain the behavioral actions of the animals, so that we can relate the neural activity …
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A Safe and Robust Multi-Agent Motion Planning Framework for Urban Air Mobility
… environment. We formulate a model-free method of learning the optimal control for the kinodynamic motion problem, and an algorithm to predict the behavior of independent or adversarial agents in the environment through a cognitive hierarchy approach. Additionally, we use repeated observations of …
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Optimal Control for Autonomous Motor Behavior
… algorithms that allow robots to generate optimal behavior from first principles. Instead of hard-coding every desired behavior, we encode the task as a cost function, and use numerical optimization to find action sequences that can accomplish the task. Using the theoretical framework of …