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Becker, Simion (author)As the world is currently actively trying to reduce the consumption of fossil fuels, large investments are done in renewable energy sources and ways are sought after to electrify fossil fuel-intensive sectors. In line with these developments, the number of electric vehicles requiring access to the electric power grid has exploded putting...master thesis 2022
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Klerk, W.J. (author)Many countries rely on flood defence systems to prevent economic damage and loss-of-life due to catastrophic floods. Asset managers of flood defence systems need to cope with the consequences of structural degradation, and changing societal and environmental conditions, in order to satisfy performance requirements and optimize societal value of...doctoral thesis 2022
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Nota, Hugo (author)The effects of climate change are felt all around the world. An increased sea level goes hand in hand with an increased risk of flooding. To combat this, the coastlines must be reinforced to withstand future sea levels. However, repeatedly reinforcing coastlines to keep up with the sea level rise (SLR) could prove extremely costly. An...master thesis 2022
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van Zijl, Job (author)Deep Reinforcement Learning (DRL) shows great potential for flight control, due to its adaptability, fault-tolerance, and as it does not require an accurate system model. However, these techniques, like many machine learning applications, are considered black-box as their inner workings are hidden. This paper aims to break open the black box of...master thesis 2022
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Interpretability and performance comparisons of decision tree surrogate models produced by AGGREVATEWols, Jonathan (author)Imitation learning algorithms, such as AggreVaTe, have proven successful in solving many challenging tasks accurately and efficiently. In practice, however, they have not been applied quite as much. Black box policies produced by imitation learning algorithms can not ensure the safety needed for real-world applications. This paper extends this...bachelor thesis 2022
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Meijer, Caspar (author)Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...bachelor thesis 2022
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de Vringer, Stefan (author)Realistic vehicle routing problems have been highly relevant for years in a wide variety of domains. One such domain is food delivery, where well-crafted routes can reduce costs and contribute to customer satisfaction. This thesis formulates a problem variant for the restaurant meal delivery problem in order to examine the reoptimization of meal...master thesis 2022
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Remmerswaal, Willemijn (author)Both model predictive control (MPC) and reinforcement learning (RL) have shown promising results in the control of traffic signals in urban traffic networks. There are, however, a few drawbacks. MPC controllers are not adaptive and therefore perform suboptimal in the presence of the uncertainties that always occur in urban traffic systems....master thesis 2022
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LU, Jingyi (author)Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning...master thesis 2022
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Saaybi, Serge (author)Robotic agents can continuously provide feedback to people based on their behaviors. For instance, a robot swarm can remind a group of people to respect social distancing guidelines during a pandemic or discourage unwanted behavior such as littering. However, developing a swarm robot to operate in realistic situations is challenging: a robot...master thesis 2022
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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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Mustafa, S. (author), Singh, S. (author), Hordijk, D. (author), Schlangen, E. (author), Lukovic, M. (author)Hybrid application of conventional concrete and Strain Hardening Cementitious Composite (SHCC) is recently shown to be promising for crack width control. In this paper, a combined experimental and numerical study is performed to validate the concept and to study the effect of interface treatment on crack width control. The interface is varied...journal article 2022
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Badea, C. (author), Groot, D.J. (author), Morfin Veytia, A. (author), Ribeiro, M.J. (author), Dalmau, Ramon (author), Ellerbroek, J. (author), Hoekstra, J.M. (author)Air traffic demand has increased at an unprecedented rate in the last decade (albeit interrupted by the COVID pandemic), but capacity has not increased at the same rate. Higher levels of automation and the implementation of decision-support tools for air traffic controllers could help increase capacity and catch up with demand. The air traffic...conference paper 2022
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Peschl, M. (author), Zgonnikov, A. (author), Oliehoek, F.A. (author), Cavalcante Siebert, L. (author)Inferring reward functions from demonstrations and pairwise preferences are auspicious approaches for aligning Reinforcement Learning (RL) agents with human intentions. However, state-of-the art methods typically focus on learning a single reward model, thus rendering it difficult to trade off different reward functions from multiple experts. We...conference paper 2022
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Andriotis, C. (author), Papakonstantinou, K.G. (author)Inspection and maintenance (I&M) optimization entails many sources of computational complexity, among others, due to high-dimensional decision and state variables in multi-component systems, long planning horizons, stochasticity of objectives and constraints, and inherent uncertainties in measurements and models. This paper studies how the...conference paper 2022
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Groot, D.J. (author), Ribeiro, M.J. (author), Ellerbroek, J. (author), Hoekstra, J.M. (author)Current estimates show that the presence of unmanned aviation is likely to grow exponentially over the course of the next decades. Even with the more conservative estimates, these expected high traffic densities require a re-evaluation of the airspace structure to ensure safe and efficient operations. One structure that scored high on both the...conference paper 2022
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Suau de Castro, M. (author), He, J. (author), Spaan, M.T.J. (author), Oliehoek, F.A. (author)Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and the pace at which that data can be obtained. In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for...conference paper 2022
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Vergara Barrios, P.P. (author), Salazar, Mauricio (author), Giraldo, Juan S. (author), Palensky, P. (author)In this paper, a Reinforcement Learning (RL)-based approach to optimally dispatch PV inverters in unbalanced distribution systems is presented. The proposed approach exploits a decentralized architecture in which PV inverters are operated by agents that perform all computational processes locally; while communicating with a central agent to...journal article 2022
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Baldi, S. (author), Zhang, Z. (author), Liu, Di (author)We propose a new reinforcement learning method in the framework of Recursive Least Squares-Temporal Difference (RLS-TD). Instead of using the standard mechanism of eligibility traces (resulting in RLS-TD((Formula presented.))), we propose to use the forgetting factor commonly used in gradient-based or least-square estimation, and we show that...journal article 2022
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Huang, Y. (author), Grunewald, S. (author), Schlangen, E. (author), Lukovic, M. (author)Ultra-High Performance Fiber-Reinforced Concrete (UHPFRC) is, due to its superior mechanical properties and low permeability, a promising material for the restoration and improvement of the mechanical resistance and durability of existing Reinforced Concrete (RC) structures. This paper reviews the strengthening applications of UHPFRC in flexure,...review 2022