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Volkers, Bas (author)
Individualizing mechanical ventilation treatment regimes remains a challenge in the intensive care unit (ICU). Reinforcement Learning (RL) offers the potential to improve patient outcomes and reduce mortality risk, by optimizing ventilation treatment regimes. We focus on the Offline RL setting, using Offline Policy Evaluation (OPE), specifically...
master thesis 2024
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Bink, Kiki (author)
Facing the critical challenge of reducing greenhouse gas (GHG) emissions in the maritime industry, this thesis explores the potential of smart control systems using Reinforcement Learning (RL) for autonomous sailing. Traditional controls for sailing fall short in navigating the complex, dynamic conditions of maritime environments. RL has shown...
master thesis 2024
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Li, LITIAN (author)
This project explores adaptation to preference shifts in Multi-objective Reinforcement Learning (MORL), with a focus on how Reinforcement Learning (RL) agents can align with the preferences of multiple experts. This alignment can occur across various scenarios featuring distinct preferences of experts or within a single scenario that experiences...
master thesis 2024
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Winckler Musskopf, Nicolas (author)
The scheduling of engine shop visits quickly becomes a complex problem to solve as the number of aircraft and engines increases. In recent times, different approaches have been used to tackle this problem and optimize schedules, reducing costs and increasing revenue. This paper formulates the ESV scheduling problem as a Markov Decision Process...
master thesis 2024
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Caranti, Leonardo (author)
This Master Thesis investigates the possible improvements to the Target Time Management concept to optimize the arrival flows for SWISS International Airlines. The aim is to improve operational performance based on the current model used, as well as prove that Target Time Management constitutes a valuable system to improve operations in a...
master thesis 2024
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Homola, Marek (author)
In the rapidly evolving aviation sector, the quest for safer and more efficient flight operations has historically relied on traditional Automatic Flight Control Systems (AFCS) based on high-fidelity models. However, such models not only incur high development costs but also struggle to adapt to new, complex aircraft designs and unexpected...
master thesis 2024
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Holman, Storm (author)
In response to the increasing challenges of Cyber Electromagnetic Activities (CEMA) in urban settings, characterized by dense electromagnetic (EM) signals and rising data traffic, this research introduces an Agent-Based Model (ABM) aimed at prioritizing critical signals. The primary goal of this research is to deploy a Unmanned Aerial Vehicle ...
master thesis 2024
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Meppelink, Geert jan (author)
The growing demand for electricity, driven by widespread adoption of heat pumps, electric vehicles, and industrial electrification, strains power grids and introduces challenges for a reliable and secure supply amidst intermittent renewable energy integration. Network topology control offers flexibility, altering connections to redirect power...
master thesis 2023
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van Leeuwen, Sander (author)
Language is an intuitive and effective way for humans to communicate. Large Language Models (LLMs) can interpret and respond well to language. However, their use in deep reinforcement learning is limited as they are sample inefficient. State-of-the-art deep reinforcement learning algorithms are more sample efficient but cannot understand...
master thesis 2023
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Magri, Federico (author)
In this study, we present a first step towards a cutting-edge software framework that will enable autonomous racing capabilities for nano drones. Through the integration of neural networks tailored for real-time operation on resource-constrained devices. A lightweight Convolutional Neural Network, with the Gatenet architecture, is adjusted for...
master thesis 2023
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Deivamani, Karthikeyan (author)
The increasing adoption of renewable energy sources, particularly photovoltaic (PV) systems in residential sectors has raised important energy balancing challenges due to the intermittent nature of energy generation. To address these challenges and prioritize cost savings for residential consumers, this research investigates the integration of...
master thesis 2023
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Dierikx, Martin (author)
To reduce morbidity and mortality caused by multiple chronic conditions, the number of steps people take each day should be gradually increased. For this, a recommended step goal can be created that is based on an individual's previous walking behaviour. However, for a person, the achievability of this recommended goal can change daily because...
master thesis 2023
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Valencia Ibañez, Santiago (author)
System architecting is one of the first stages of the engineering problem-solving process. Pivotal decisions regarding the system's overall configuration are taken in this phase. Consequently, decision support tools like system architecture optimization are needed to effectively assess the architectural design space. However, system architecture...
master thesis 2023
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de Gooijer, Jessica (author)
Reinforcement learning, especially deep reinforcement learning, has made many advances in the last decade. Similarly, great strides have been made in multi-agent reinforcement learning. Systems of cooperative autonomous robots are increasingly being used, for which multi-agent reinforcement learning can be used as a training method. However, the...
master thesis 2023
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Tan, Martin (author)
In the field of Systems and Control, optimal control problem-solving for complex systems is a core task. The development of accurate mathematical models to represent these systems’ dynamics is often difficult. This complexity comes from potential uncertainties, complex non-linearities, or unknown factors that might affect the system. Because of...
master thesis 2023
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Atanassov, Vassil (author)
Legged animals possess extraordinary agility with which they can gracefully traverse a wide range of environments, from running through grasslands to jumping across cliffs and climbing nearly vertical walls. Inspired by this, in this work, we use Deep Reinforcement Learning to give legged robots the ability to perform a diverse set of highly...
master thesis 2023
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Whitman, Charles (author)
This work seeks to resolve an outstanding problem in the use of reinforcement-learning methods for the simulation of economically-rational agents. We discuss the problem of non-stationarity, and how this subsequently limits market simulation capabilities. After explicating and isolating the source of the problem for a day-ahead electricity...
master thesis 2023
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Stripp, Sebastian (author)
Current building operations can be improved through smart predictive operation based on weather and use patterns in order to save energy with minimal impact on the building fabric and daily use. The existing literature has investigated implementations, and potential savings through combining with variable tariffs, however, this thesis addresses...
master thesis 2023
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Liu, Yuxiang (author)
Machine learning can be effectively applied in control loops to robustly make optimal control decisions. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering, because SNNs can potentially offer high energy efficiency and new SNN-enabling neuromorphic hardwares are being...
master thesis 2023
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Keijzer, Alexander (author)
Experience replay for off-policy reinforcement learning has been shown to improve sample efficiency and stabilize training. However, typical uniformly sampled replay includes many irrelevant samples for the agent to reach good performance. We introduce Action Sensitive Experience Replay (ASER), a method to prioritize samples in the replay buffer...
master thesis 2023
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