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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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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

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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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Distributional Reinforcement Learning for Flight Control: A risk-sensitive approach to aircraft attitude control using Distributional RL

Seres, Peter (author)

With the recent increase in the complexity of aerospace systems and autonomous operations, there is a need for an increased level of adaptability and model-free controller synthesis. Such operations require the controller to maintain safety and performance without human intervention in non-static environments with partial observability and...

master thesis 2022

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Toward Reliable Robot Navigation Using Deep Reinforcement Learning

van Rietbergen, Tomas (author)

Reliable indoor navigation in the presence of dynamic obstacles is an essential capability for mobile robot deployment. Previous work on robot navigation focuses on expanding the network structure and hardware setup leading to more complex and costly systems. The accompanying physical demonstrations are often limited to slow-moving agents and...

master thesis 2022

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Deep Reinforcement Learning for the Automatic Six-Degree-of-Freedom Docking Maneuver of Space Vehicles

Casals Sadlier, Juliette (author)

The implementation of a model-free, off-policy, actor-critic deep reinforcement learning algorithm consistent of two separate agents to a six-degree-of freedom spacecraft docking maneuver to develop a control policy is carried out in the research presented in this article. Reinforcement learning has the ability to learn without instruction, this...

master thesis 2022

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Spinning-off the AI: Defining the Value Proposition of an Artificial Intelligence solution

Drozdowski, Tomasz (author)

Artificial Intelligence technology offers computational, decision-making, and optimizing abilities that surpass every previously established traditional computation method. By being able to navigate across large amounts of data, the realized solutions learn on their own and provide results that would be unattainable with other ways. The complex...

master thesis 2022

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An iterative deep reinforcement learning approach to solve vehicle routing problems

de Brouchoven de Bergeyck, Aymar (author)

Vehicle routing problems have been studied for more than 50 years, and their in- terest has never been higher. It is partly due to their significant economic impact. Decreasing the traveling time, certainly for big organizations, can save costs in the range of millions of dollars and increase their service quality. Moreover, the wide variety of...

master thesis 2022

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Explainable Reinforcement Learning in Flight Control through Reward Decomposition

Ferreira Lemos, André (author)

Even though Deep Reinforcement Learning (DRL) techniques have proven their ability to solve highly complex control tasks, the opaqueness and inexplicability associated with these solutions many times stops them from being applied to real flight control applications. In this research, reward decomposition explanations are used to tackle this...

master thesis 2022