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Foffano, Daniele (author)Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usually formalised as Markov Decision Processes, using a model of the environment dynamics to compute the optimal policy. When dealing with complex environments, the environment dynamics are frequently approximated with function approximators (such as...master thesis 2022
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Dimanidis, Ioannis (author)We propose a novel method combining elements of supervised- and Q-learning for the control of dynamical systems subject to unknown disturbances. By using the Inverse Optimization framework and in-hindsight information we can derive a causal parametric optimization policy that approximates a non-causal MPC expert. Furthermore, we propose a new...master thesis 2021
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Köstler, Klemens (author)In this paper, we propose and analyze a q-learning-based approach for allocation of operators to security teams in order to improve operational efficiency of an airport security checkpoint. The research is composed of two parts. First, we develop an agent-based model capable of simulating an airport security checkpoint. Second, we introduce...master thesis 2021
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Bello, Riccardo (author)The demand of adding fault tolerance to quadcopter control systems has significantly increased with the rise of adoption of UAVs in numerous sectors. This work proposes and demonstrates the use of Hierarchical Reinforcement Learning to control a quadcopter subject to severe actuator fault. State-of-the-art algorithms are implemented, and a...master thesis 2021
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Smit, Jordi (author)Offline reinforcement learning, or learning from a fixed data set, is an attractive alternative to online reinforcement learning. Offline reinforcement learning promises to address the cost and safety implications of taking numerous random or bad actions online, which is a crucial aspect of traditional reinforcement learning that makes it...master thesis 2021
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de Vries, Yorick (author)With the increasing global demand for logistics, supply chains have grown a lot in volume over the last decades. To be able to operate effectively within the capacity constraints of the carriers, proper collaboration and optimization of order allocation is required. Van Berkel Logistics facilitates the transport of containers by trucks from sea...master thesis 2021
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Ge, Zhouxin (author)Aircraft with disruptive designs have no high-fidelity and accurate flight models. At the same time, developing models for stochastic phenomena for traditional aircraft configurations are costly, and classical control methods cannot operate beyond the predefined operation points or adapt to unexpected changes to the aircraft. The Proximal Policy...master thesis 2021
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Weijs, George (author)Bus bunching is a problem that occurs in many high frequent bus systems. This can be averted by several countermeasures of which holding control is the most popular one in practice. Holding control strategies are often implemented using predefined rules. In this study, multi-agent reinforcement learning is selected to develop an effective...master thesis 2021
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Kreynen, Bernd (author)Dementia care is a growing problem, both due to a rising number of cases and due to a shortage in healthcare workers. Aside from cognitive symptoms persons with dementia (PwDs) often deal with psychological symptoms such as agitation. The individualized music intervention (IMI) by Linda Gerdner has been proposed to reduce these. This is the...master thesis 2021
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Zoon, Job (author)In the past few years, there has been much research in the field of Autonomous Vehicles (AV). If AVs are implemented in our daily lives, this could have many advantages. Before this can happen, safe driver models need to be designed which control the AVs. One technique that is suitable to create these models is Reinforcement Learning (RL). A...master thesis 2021
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Daniel Noel, Alejandro (author)Intelligent agents must pursue their goals in complex environments with partial information and often limited computational capacity. Reinforcement learning methods have achieved great success by creating agents that optimize engineered reward functions, but which often struggle to learn in sparse-reward environments, generally require many...master thesis 2021
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Hermans, Max (author)The current ATC system is seen as the most significant limitation to coping with an increased air traffic density. Transitioning towards an ATC system with a high degree of automation is essential to cope with future traffic demand of the airspace. In recent studies, reinforcement learning has shown promising results automating Conflict...master thesis 2021
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De Buysscher, Diego (author)Safe Curriculum Learning constitutes a collection of methods that aim at enabling Rein- forcement Learning (RL) algorithms on complex systems and tasks whilst considering the safety and efficiency aspect of the learning process. On the one hand, curricular reinforce- ment learning approaches divide the task into more gradual complexity stages to...master thesis 2021
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Geursen, Izaak (author)Current state-of-the-art airline planning models are required to decrease models either in size or complexity due to computational limitations, limiting the <br/>operational applicability to problems of representative sizes. Models return suboptimal solutions, especially when confronted with factors of uncertainty. Considering the growing...master thesis 2021
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Willemsen, Daniël (author)Multi-agent robotic systems could benefit from reinforcement learning algorithms that are able to learn behaviours in a small number trials, a property known as sample efficiency. This research investigates the use of learned world models to create more sample-efficient algorithms. We present a novel multi-agent model-based reinforcement...master thesis 2021
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Krijnen, Bas (author)The growing need for CubeSats could present strong demands for the use of automated systems during the early stage of the design cycle. Automated design tools that are able to incorporate the entire design space offered by the commercial-off-the-shelf (COTS) components for CubeSats may potentially improve the design of a CubeSat, compared to...master thesis 2020
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Miloševiċ, Stevan (author)Reinforcement Learning (RL) methods have become a topic of interest for performing guidance and navigation tasks, due to potential adaptability and autonomy improvements within dynamic systems. Nevertheless, a core component of RL is an agent exploring the environment it finds itself in, resulting in an intrinsic violation of the agent's safety....master thesis 2020
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Luijkx, Jelle (author)There are many stages that involve humans handling food objects in the processing chains from farms to stores. For some of these tasks it is desirable to look for a robotic solution to either assist the human or even take over that task, e.g. if it is physically demanding, imposes contamination risks or because of economical considerations....master thesis 2020
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Cornet, R. (author)Fully automated vehicles have the potential to increase road safety and improve traffic flow by taking the human element out of the driving loop. They can also provide mobility to people who are unable to operate a conventional vehicle. Safe automated vehicles must be able to respond in emergency situations or drive on slippery roads in bad...master thesis 2020
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Schneider, C.C.N. (author)Signalized urban intersections are bottlenecks for traffic and cause congestion. To improve traffic signal plans, research efforts have been made to create self-adaptive traffic controllers, i.e. controllers which adapt in real-time to the current traffic demand based on connected vehicle data. Past research on self-adaptive controllers has...master thesis 2020