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Vonk, Bart (author)
Research on reinforcement learning algorithms to play complex video games have brought forth controllers surpassing human performance. This paper explores the possibilities of applying these techniques to the sequencing and spacing of aircraft. Two experiments are performed. First a single aircraft must learn to fly a 4D trajectory using only...
master thesis 2019
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Fris, Rein (author)
Deep Reinforcement Learning (DRL) enables us to design controllers for complex tasks with a deep learning approach. It allows us to design controllers that are otherwise cumbersome to design with conventional control methodologies. Often, an objective for RL is binary in nature. However, exploring in environments with sparse rewards is a problem...
master thesis 2020
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van Zeumeren, Ilse (author)
This project is focussed on a design for stimulating interaction amongst passengers in the Seabubble. The Seabubble is an autonomous hydrofoil that is able to fly above water. The Seabubble can transport 4 to 5 passengers on inland waterways. The hydrofoil-technology creates a whole new travel-experience because it provides very stable and...
master thesis 2020
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de Lange, Rudy (author)
This thesis proposes the novel Behaviour Tree Update Framework (BTUF) for the initial construction and continuous incremental adaptation of Behaviour Trees (BTs) for applications in Learning from Demonstration (LfD) frameworks to create complex robot behaviours associated with Activities of Daily Living (ADL) without requiring the user to have a...
master thesis 2022
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