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Uğurlu, Irem (author)Automated driving is a rapidly growing technology nowadays. Semi-automated driving is a subpart of automated driving which has multiple driving modes where both driver and automated module can take control. But full safety and comfort guarantees cannot still be given to the drivers. In this project, research has been done to ensure driver safety...bachelor thesis 2021
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Latoškinas, Evaldas (author)Semi-autonomous driving innovations aim to bridge the gap to fully autonomous driving by co-operating with human drivers to lead to optimal choices on who should drive in different scenarios by offering different automation levels. However, in the present day, known semi-autonomous driving solutions do not generalise to every complex case of...bachelor thesis 2021
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Velthoven, Tim (author)Robot soccer competitions have been around for a while and have been a great environment to develop AI algorithms in. One of these environments is the AI world cup. The AI world cup environment is a virtual environment where two teams with five robots each play a soccer match. This paper focuses on defending the attacker that is carrying the...bachelor 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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Tang, Shi Yuan (author), Oliehoek, F.A. (author), Irissappane, Athirai A. (author), Zhang, Jie (author)Cross-Entropy Method (CEM) is a gradient-free direct policy search method, which has greater stability and is insensitive to hyperparameter tuning. CEM bears similarity to population-based evolutionary methods, but, rather than using a population it uses a distribution over candidate solutions (policies in our case). Usually, a natural...conference paper 2021
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Li, Guangliang (author), Whiteson, Shimon (author), Dibeklioğlu, Hamdi (author), Hung, H.S. (author)Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this paper, we investigate the potential of agent learning from trainers’ facial expressions via...conference paper 2021
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Igl, Maximilian (author), Farquhar, Gregory (author), Luketina, Jelena (author), Böhmer, J.W. (author), Whiteson, Shimon (author)Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is often not explicitly addressed in deep RL and a single neural network is continually...conference paper 2021
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Yang, Q. (author), Simão, T. D. (author), Tindemans, Simon H. (author), Spaan, M.T.J. (author)Safe exploration is regarded as a key priority area for reinforcement learning research. With separate reward and safety signals, it is natural to cast it as constrained reinforcement learning, where expected long-term costs of policies are constrained. However, it can be hazardous to set constraints on the expected safety signal without...conference paper 2021
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Rijsdijk, J. (author), Wu, L. (author), Perin, G. (author), Picek, S. (author)Deep learning represents a powerful set of techniques for profiling side-channel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional neural networks give strong attack performance where it is possible to break targets protected with various coun-termeasures....journal article 2021
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Muench, C. (author), Oliehoek, F.A. (author), Gavrila, D. (author)Modeling possible future outcomes of robot-human interactions is of importance in the intelligent vehicle and mobile robotics domains. Knowing the reward function that explains the observed behavior of a human agent is advantageous for modeling the behavior with Markov Decision Processes (MDPs). However, learning the rewards that determine...journal article 2021
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Coppens, Youri (author), Steckelmacher, Denis (author), Jonker, C.M. (author), Nowe, A.S.P. (author)Today’s advanced Reinforcement Learning algorithms produce black-box policies, that are often difficult to interpret and trust for a person. We introduce a policy distilling algorithm, building on the CN2 rule mining algorithm, that distills the policy into a rule-based decision system. At the core of our approach is the fact that an RL...conference paper 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