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Reinforcement Learning for Flight Control: Evaluating Handling Qualities and Stability Properties of the PH-LAB

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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Learning Vision-based Navigation Policies for Information Gathering with Quadcopters: A Deep Reinforcement Learning Approach

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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Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs

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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Learning-Based Multi-UAV Flocking Control With Limited Visual Field and Instinctive Repulsion

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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Synergetic-informed deep reinforcement learning for sustainable management of transportation networks with large action spaces

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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An adaptive agent-based approach for instant delivery order dispatching: Incorporating task buffering and dynamic batching strategies

Lu, Miaojia (author), Yan, Xinyu (author), Sharif Azadeh, S. (author), Wang, P. (author)

The volume of instant delivery has witnessed a significant growth in recent years. Given the involvement of numerous heterogeneous stakeholders, instant delivery operations are inherently characterized by dynamics and uncertainties. This study introduces two order dispatching strategies, namely task buffering and dynamic batching, as...

journal article 2024

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Analysis of the impact of traffic density on training of reinforcement learning based conflict resolution methods for drones

Groot, D.J. (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)

Conventional Air Traffic Control is still predominantly being done by human Air Traffic Controllers, however, as the traffic density increases, the workload of the controllers increases as well. Especially for the area of unmanned aviation, driven by the rise in drones, having human controllers might become unfeasible. One of the methods that...

journal article 2024

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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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Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling

Song, Yanjie (author), Ou, Junwei (author), Pedrycz, Witold (author), Suganthan, Ponnuthurai Nagaratnam (author), Wang, X. (author), Xing, Lining (author), Zhang, Yue (author)

Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem ...

journal article 2024

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General Reinforcement Learning Agents for Crop Management

Theocharis, Thanasis (author)

Agriculture plays a vital role in the global economy, providing the necessary food and resources for human survival. With the world’s population projected to surge, the demand for food is set to escalate in the coming decades. This increasing demand, coupled with the challenges posed by climate change and the detrimental effects of pollution due...

master thesis 2023

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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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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

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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

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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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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

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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

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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