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

document

Prioritizing states with action sensitive return in experience replay

Keijzer, Alexander (author)

Experience replay for off-policy reinforcement learning has been shown to improve sample efficiency and stabilize training. However, typical uniformly sampled replay includes many irrelevant samples for the agent to reach good performance. We introduce Action Sensitive Experience Replay (ASER), a method to prioritize samples in the replay buffer...

master thesis 2023

document

Scheduling a Flexible Manufacturing System: A reinforcement learning based approach

Pennings, Casper (author)

A flexible manufacturing system (FMS) has advantages over traditional manufacturing systems due to its ability to deal with unpredicted circumstances such as changes in demand or component breakdowns by re-routing. However, this flexibility increases the complexity of controlling such a system. Traditionally, the system model is simplified to...

master thesis 2023

document

Is human-in-the-loop reinforcement learning enhanced if the robot emotes its learning progress?: An experimental study

Lijcklama à Nijeholt, Floortje (author)

As technology continues to evolve at a rapid pace, robots are becoming an increasingly common sight in our daily lives. <br/>Robots that work with humans need to adapt to a variety of users and tasks, and learn to optimise their behaviour. For non-specialist users to interact with such robots, the robot's learning process needs to be transparent...

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

document

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

Personalized and Adaptive Cognitive Human-Robot Interaction with a Novel Fuzzy Logic Control and Reinforcement Learning-Based Paradigm: Master Thesis Report

Munster, Marcel (author)

An aging population puts a pressure on health-care workers working with dementia patients globally. A potential solution is to provide care with Socially Assistive Robots (SARs), i.e. robots who help people through social interaction. However, for effective care these SARs must be able to personalize their behavior to individual patients and...

master thesis 2023

document

Optimised State-Dependent Sampling Control for Heavy-Haul Trains

Breysens, G. (author)

This thesis investigates the potential of state-dependent sampling strategies (SDSS) for the control of heavy-haul trains. Event-triggered control (ETC) is a control approach in which data is only sent when some state-dependent condition, the triggering condition, is satisfied. In this way, the number of communications required to stabilise a...

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

document

On the road from Model-Based Dynamical Programming to Model-Free Reinforcement Learning: A sample efficient approach

Mur Uribe, Pol (author)

This thesis introduces a new method, called Mixed Iteration, for controlling Markov Decision Processes when partial information is known about the dynamics of the Markov Decision Process. The algorithm uses sampling to calculate the expectation of partially known dynamics in stochastic environments. Its goal is to lower the number of iterations...

master thesis 2023

document

Combining Multi-Objective Planning with Reinforcement Learning to Solve Complex Tasks in Environments with Sparse Rewards

van Rijn, Cas (author)

Sequential decision-making problems are problems where the goal is to find a sequence of actions that complete a task in an environment. A particularly difficult type of sequential decision-making problem to solve is one in which the environment has sparse rewards, a large state space, and where the goal is to complete a complex task. In this...

master thesis 2023

document

Robotic Packaging Optimization with Reinforcement Learning and Real-World Data

Drijver, Eveline (author)

Intelligent manufacturing has become increasingly important in the food packaging industry due to the growing demand for enhanced productivity and flexibility while minimizing waste and lead times. This work explores the integration of such manufacturing in automated secondary robotic food packaging solutions that transfer food products into...

master thesis 2023

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Teaching bimanual dexterous manipulations with interactive demonstrations and reinforcement learning

Du, Zheyu (author)

Robot dexterous manipulation research has drawn more attention in recent years since the development of various learning methods makes it possible for robots to achieve dexterity at the human level. Many attempts have been made to integrate human knowledge into Reinforcement Learning (RL) processes for faster learning speed and better...

master thesis 2023

document

Conflict Resolution at High Traffic Densities with Reinforcement Learning

Ribeiro, M.J. (author)

Increasing delays and congestion reported in many aviation sectors indicate that the current centralised operational model is rapidly approaching saturation levels. Air Traffic Control (ATC) system is not expected to keep pace with the ever-increasing demand for air transportation. Its capacity is still limited by the available controllers, and...

doctoral 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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Modelling Agents with Variational Autoencoders in Multi-Agent Sequential Decision Making

Lenferink, Luc (author)

The ability to model other agents can be of great value in multi-agent sequential decision making problems and has become more accessible due to the introduction of deep learning into reinforcement learning. In this study, the aim is to investigate the usefulness of modelling other agents using variational autoencoder based models in partially...

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

document

Spiking Neural-Networks-Based Data-Driven Control

Liu, Y. (author), Pan, W. (author)

Machine learning can be effectively applied in control loops to make optimal control decisions robustly. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering because SNNs can potentially offer high energy efficiency, and new SNN-enabling neuromorphic hardware is being...

journal article 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

document

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&amp;M) processes. Most available methods simplify the I...

journal article 2023