document

Condition-Based Maintenance scheduling of an aircraft fleet under partial observability: A Deep Reinforcement Learning approach

Tseremoglou, I. (author), Santos, Bruno F. (author)

In the Condition-Based Maintenance (CBM) context, the definition of optimal maintenance plans for an aircraft fleet depends on an efficient integration of : (i) the probabilistic predictions of the health condition of the components and (ii) the stochastic arrival of the corrective maintenance tasks, together with consideration of the...

journal article 2024

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Deep Reinforcement Learning for Aircraft Landing: A study on the use of Deep Reinforcement Learning techniques for automatic control of aircraft landing

Herren Aguillar van de Laar, Thomas (author)

master thesis 2023

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Comparative Analysis of Exploration Algorithms in Deep Reinforcement Learning for Autonomous Driving: How does epsilon-greedy, random network distillation, bootstrapped DQN affect training and the robustness of final policies under various testing conditions in autonomous driving?

Sozen, Efe (author)

Autonomous driving is a rapidly evolving field that aims to enhance road safety and reduce accidents through the use of advanced software and hardware technologies. Reinforcement learning (RL) combined with deep neural networks has emerged as a promising approach for training autonomous agents. This research paper investigates three exploration...

bachelor thesis 2023

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Evaluating robustness of deep reinforcement learning for autonomous driving: Effects of domain randomization on training and robustness

Bayram, Ege (author)

Deep reinforcement learning has been a topic of research in recent years and has been expanding into the domain of autonomous driving. As autonomous driving is likely to involve people, such as daily commuters, it is necessary to ensure the machine will perform well enough in real-life environments not to put anyone at risk. There exist possible...

bachelor thesis 2023

document

Evaluating Robustness of Deep Reinforcement Learning for Autonomous Driving: How does entropy maximization affect the training and robustness of final policies under various testing conditions?

Ortal, Bartu (author)

This research paper aims to investigate the effect of entropy while training the agent on the robustness of the agent. This is important because robustness is defined as the agent's adaptability to different environments. A self-driving car should adapt to every environment that it is being used in since a mistake could cost someone's life....

bachelor thesis 2023

document

Improving DRL Of Vision-Based Navigation By Stereo Image Prediction

den Ridder, Luc (author)

Although deep reinforcement learning (DRL) is a highly promising approach to learning robotic vision-based control, it is plagued by long training times. This report introduces a DRL setup that relies on self-supervised learning for extracting depth information valuable for navigation. Specifically, a literature study is conducted to investigate...

master thesis 2023

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Multi-objective Deep Reinforcement Learning for predictive maintenance of road networks

Krachtopoulos, Konstantinos (author)

Operation and maintenance of the built environment have a major effect on socioeconomic stability and sustainability. A significant part of our built world approaches or has well exceeded its designated structural life. As engineers, we need to find efficient ways to extend this life while maintaining acceptable levels of safety and performance....

master thesis 2023

document

Learning to search & track dynamic targets with graph representations

Cong, Shijie (author)

Autonomous robots have been widely applied to search and rescue missions for information gathering about target locations. This process needs to be continuously replanned based on new observations in the environment. For dynamic targets, the robot needs to not only discover them but also keep tracking their positions. Previous works focus on...

master thesis 2023

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Incorporating climate change in I&M planning for engineering structures - A Deep Reinforcement Learning Framework

Knoppert, Sammie (author)

In the last decades, climate change is causing our environment to change rapidly, unprecedented in recent history. Civil engineering structures are dependent on the deteriorating environment they are situated in. Changes can cause an increase in loading due to, for example, extreme weather events or alter the structure’s resistance by, for...

master thesis 2023

document

Autonomous conflict resolution for high-density urban operations using deep reinforcement learning

Santana, Ricardo (author)

Low-altitude, high-density air traffic is expected to grow in the coming decades with several companies being certified to initiate urban operations for both freight and passenger transport. However, traditional human-centered Air Traffic Control operations (ATCos) are not scalable to handle the increased demand to maintain safe separation...

master thesis 2023

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Controlling the estimation bias in deep reinforcement learning problems with sparse rewards: Towards robust robotic object manipulation learning

Varga, Roland (author)

Many recent robot learning problems, real and simulated, were addressed using deep reinforcement learning. The developed policies can deal with high-dimensional, continuous state and action spaces, and can also incorporate machine-generated or human demonstration data. A great number of them depend on state-action value estimates, especially the...

master thesis 2023

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Evolutionary Reinforcement Learning: A Hybrid Approach for Safety-informed Intelligent Fault-tolerant Flight Control

Gavra, Vlad (author)

Recent research in bio-inspired artificial intelligence potentially provides solutions to the challenging problem of designing fault-tolerant and robust flight control systems. The current work proposes SERL, a novel Safety-informed Evolutionary Reinforcement Learning algorithm, which combines Deep Reinforcement Learning (DRL) and neuro...

master thesis 2023

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A Comparative Study of Optimization Models for Condition-Based Maintenance Scheduling of an Aircraft Fleet

Tseremoglou, I. (author), van Kessel, Paul J. (author), Santos, Bruno F. (author)

Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type of problem falls under the broad category of...

journal article 2023

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The first AI4TSP competition: Learning to solve stochastic routing problems

Zhang, Yingqian (author), Bliek, Laurens (author), da Costa, Paulo (author), Refaei Afshar, Reza (author), Reijnen, Robbert (author), Catshoek, T. (author), Vos, D.A. (author), Verwer, S.E. (author), Schmitt-Ulms, Fynn (author)

This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCAI-21). The TSP is one of the classical combinatorial optimization problems, with many variants inspired by real-world applications. This first competition asked the...

journal article 2023

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Comfort-Oriented Motion Planning for Automated Vehicles Using Deep Reinforcement Learning

Rajesh, Nishant (author), Zheng, Y. (author), Shyrokau, B. (author)

Automated vehicles promise numerous advantages to their users. The proposed benefits could however be overshadowed by a rise in the susceptibility of passengers to motion sickness due to their engagement in non-driving tasks. Increasing attention is paid to designing vehicle motion to mitigate motion sickness. In this work, the deep...

journal article 2023

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Federated Learning for Online Resource Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach

Zheng, Jingjing (author), Li, Kai (author), Mhaisen, N. (author), Ni, Wei (author), Tovar, Eduardo (author), Guizani, Mohsen (author)

Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a...

conference paper 2023

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Deep reinforcement learning for cerebral anterior vessel tree extraction from 3D CTA images

Su, Jiahang (author), Li, Shuai (author), Wolff, Lennard (author), van Zwam, Wim (author), Niessen, W.J. (author), van der Lugt, Aad (author), van Walsum, T. (author)

Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to treatment decision making. The purpose of our work is to develop a method that can achieve this from routinely acquired computed tomography angiography (CTA) and...

journal article 2023

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AutoPOI: automated points of interest selection for side-channel analysis

Remmerswaal, Mick G.D. (author), Wu, L. (author), Tiran, Sébastien (author), Mentens, Nele (author)

Template attacks (TAs) are one of the most powerful side-channel analysis (SCA) attacks. The success of such attacks relies on the effectiveness of the profiling model in modeling the leakage information. A crucial step for TA is to select relevant features from the measured traces, often called points of interest (POIs), to extract the...

journal article 2023

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Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning

Morato, P. G. (author), Andriotis, C. (author), Papakonstantinou, K. G. (author), Rigo, P. (author)

In the context of modern engineering, environmental, and societal concerns, there is an increasing demand for methods able to identify rational management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I...

journal article 2023

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EAVS: Edge-assisted Adaptive Video Streaming with Fine-grained Serverless Pipelines

Hou, Biao (author), Yang, Song (author), Kuipers, F.A. (author), Jiao, Lei (author), Fu, Xiaoming (author)

Recent years have witnessed video streaming grad- ually evolve into one of the most popular Internet applications. With the rapidly growing personalized demand for real-time video streaming services, maximizing their Quality of Experience (QoE) is a long-standing challenge. The emergence of the server- less computing paradigm has potential to...

conference paper 2023