Searched for: subject%3A%22Reinforcement%255C+Learning%22
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Corvi, Giovanni (author)
The rapidly growing volume of parcel shipments is straining transportation and logistics sectors, highlighting the need for innovative solutions to optimize packing and loading processes. The online bin packing problem (BPP), an NP-hard computational problem, finds practical applications in numerous sectors, including modern packaging and...
master thesis 2024
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Jansen, Hidde (author)
Reinforcement Learning applied to flight control has shown to have several benefits over classical, linear flight controllers, as it eliminates the need for gain scheduling and it could provide fault-tolerance. The application to civil aviation in practice, however, is non-existent as there are multiple safety concerns. This research...
master thesis 2024
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Chin-A-Pauw, Laurens (author)
In this thesis, we aim to improve the application of deep reinforcement learning in portfo- lio optimization. Reinforcement learning has in recent years been applied to a wide range of problems, from games to control systems in the physical world and also to finance. While reinforcement learning has shown success in simulated environments (e.g....
master thesis 2024
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Vellekoop, Joris (author)
Deep reinforcement learning presents a compelling approach for the exploration of cluttered 3D environments, offering a balance between fast computation and effective vision-based navigation. Yet, the use of 3D navigation for learning-based information gathering remains largely unexplored. Navigation in 3D space poses the challenge of having an...
master thesis 2024
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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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Berjaoui Tahmaz, Amin (author)
This paper presents a hierarchical reinforcement learning framework for efficient robotic manipulation in sequential contact tasks. We leverage this hierarchical structure to sequentially execute behavior primitives with variable stiffness control capabilities for contact tasks. Our proposed approach relies on three key components: an action...
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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Molhoek, Jord (author)
Many real-world problems fall in the category of sequential decision-making under uncertainty; Markov Decision Processes (MDPs) are a common method for modeling such problems. To solve an MDP, one could start from scratch or one could already have an idea of what good policies look like. Furthermore, there could be uncertainty in this idea. In...
master thesis 2024
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van der Spaa, L.F. (author)
Physical human-robot cooperation (pHRC) has the potential to combine human and robot strengths in a team that can achieve more than a human and a robot working on the task separately. However, how much of the potential can be realized depends on the quality of cooperation, in which awarenes of the partner’s intention and preferences plays an...
doctoral 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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Piccini, Pietro (author)
ncentive-based demand response (iDR) programs serve as important tools for distributed system operators (DSOs) to achieve a reduction in electricity demand during periods of grid overload. During these programs, participants can decide to curtail their consumption in exchange for financial incentives. Deciding the amount of curtailment for a...
master thesis 2024
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Murti, Fahri Wisnu (author), Ali, Samad (author), Iosifidis, G. (author), Latva-aho, Matti (author)
Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to jointly reconfigure the functional splits of the base stations (BSs), locations of the virtualized central...
journal article 2024
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Bai, Chengchao (author), Yan, Peng (author), Piao, Haiyin (author), Pan, W. (author), Guo, Jifeng (author)
This article explores deep reinforcement learning (DRL) for the flocking control of unmanned aerial vehicle (UAV) swarms. The flocking control policy is trained using a centralized-learning-decentralized-execution (CTDE) paradigm, where a centralized critic network augmented with additional information about the entire UAV swarm is utilized...
journal article 2024
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Wan, Z. (author), Xu, Y. (author), Chang, Z. (author), Liang, M. (author), Šavija, B. (author)
Vascular self-healing concrete (SHC) has great potential to mitigate the environmental impact of the construction industry by increasing the durability of structures. Designing concrete with high initial mechanical properties by searching a specific arrangement of vascular structure is of great importance. Herein, an automatic optimization...
journal article 2024
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Lai, Li (author), Dong, You (author), Andriotis, C. (author), Wang, Aijun (author), Lei, Xiaoming (author)
Effective transportation network management systems should consider safety and sustainability objectives. Existing research on large-scale transportation network management often employs the assumption that bridges can be considered individually under these objectives. However, this simplification misses accurate system-level representations,...
journal article 2024
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He, K. (author), Shi, S. (author), van den Boom, A.J.J. (author), De Schutter, B.H.K. (author)
Approximate dynamic programming (ADP) faces challenges in dealing with constraints in control problems. Model predictive control (MPC) is, in comparison, well-known for its accommodation of constraints and stability guarantees, although its computation is sometimes prohibitive. This paper introduces an approach combining the two methodologies...
journal article 2024
Searched for: subject%3A%22Reinforcement%255C+Learning%22
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