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Balancing multidimensional morality and progression: Evaluating the tradeoff for artificial agents playing text-based games

Şerbănescu, Bianca (author)

Morality is a fundamental concept that guides humans in the decision-making process. Given the rise of large language models in society, it is necessary to ensure that they adhere to human principles, among which morality is of substantial importance. While research has been done regarding artificial agents behaving morally, current state of the...

bachelor thesis 2023

document

Inverse Reinforcement Learning (IRL) in Presence of Risk and Uncertainty Related Cognitive Biases: To what extent can IRL learn rewards from expert demonstrations with loss and risk aversion?

Ikiz, Meric (author)

A key issue in Reinforcement Learning (RL) research is the difficulty of defining rewards. Inverse Reinforcement Learning (IRL) is a technique that addresses this challenge by learning the rewards from expert demonstrations. In a realistic setting, expert demonstrations are collected from humans, and it is important to acknowledge that these...

bachelor thesis 2023

document

What are the implications of Curriculum Learning strategy on IRL methods?: Investigating Inverse Reinforcement Learning from Human Behavior

Vlasenko, Mikhail (author)

Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on recovering the reward function using expert demonstrations. In the field of IRL, Adversarial IRL (AIRL) is a promising algorithm that is postulated to recover non-linear rewards in environments with unknown dynamics. This study investigates the...

bachelor thesis 2023

document

Multi Agent Deep Deterministic Policy Gradient for Active Wake Control

van der Schaaf, Guus (author)

In wind farms wind turbines are often placed close to each other. Each turbine generates a turbulent wake field, this field negatively affects subsequent turbines. This can cost more than 12% efficiency. To decrease this loss we can steer the turbines away from the wind direction, this will decrease the individual turbine power output, but can...

bachelor thesis 2023

document

Replacing the acquisition function in Bayesian optimization by a neural network: How effectively do meta-learned acquisition functions in Bayesian optimization perform when optimizing for control variates of unknown functions, as compared to BO with standard acquisition functions

Ramezani, Shayan (author)

Bayesian Optimization (BO) has demonstrated significant utility across numerous applications. However, due to it being designed as a universal optimizer, its performance can often be suboptimal in specialized environments. To overcome this issue, research has been conducted into the application of transfer learning for enhancing BO performance...

bachelor thesis 2023

document

Effects of Partial Observability Solver Methods on Training and Final Policies in Autonomous Driver RL: How do different methods for dealing with partial observability in the environment influence training and the robustness of final policies under various testing conditions?

Çil, Ata (author)

Autonomous driving is a complex problem that can potentially be solved using artificial intelligence. The complexity stems from the system's need to understand the surroundings and make appropriate decisions. However, there are various challenges in constructing such a sophisticated system. One of the main challenges is to make the agent learn...

bachelor thesis 2023

document

ClimAIte Control: Improving Building Operation Through AI

Stripp, Sebastian (author)

Current building operations can be improved through smart predictive operation based on weather and use patterns in order to save energy with minimal impact on the building fabric and daily use. The existing literature has investigated implementations, and potential savings through combining with variable tariffs, however, this thesis addresses...

master thesis 2023

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Spiking Neural Networks Based Data Driven Control: An Illustration Using Cart-Pole Balancing Example

Liu, Yuxiang (author)

Machine learning can be effectively applied in control loops to robustly make optimal control decisions. 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 hardwares are being...

master thesis 2023

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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

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

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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

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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

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Use Reinforcement Learning to Generate Testing Commands for Onboard Software of Small Satellites

Li, Zhuoheng (author)

Programmers usually write test cases to test onboard software. However, this procedure is time-consuming and needs sufficient prior knowledge. As a result, small satellite developers may not be able to test the software thoroughly. <br/> <br/>A promising direction to solve this problem is reinforcement learning (RL) based testing. It searches...

master thesis 2022

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Reinforcement learning for water system control: Cost optimization at IJmuiden pumping station

Lambregts, Dorien (author)

The production and consumption of electricity need to be balanced at all times. Due to the ever-growing shift towards renewable energy generation, this poses an increasingly difficult challenge. Currently, supply is regulated to maintain balance. However, there is potential to improve reliability and save costs by shifting the balancing to the...

master thesis 2022

document

Dynamic Airspace Reconfiguration with Deep Reinforcement Learning

Rowntree, Timon (author)

For future operations of unmanned aviation, even higher traffic densities than previously seen in manned aviation are expected. Previous work has shown that a vertically layered airspace design performs best at improving safety metrics such as the total number of conflicts and Losses of Separation (LoSs). Furthermore, it has been shown that...

master thesis 2022

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Reinforcement Learning for Flight Control: Hybrid Offline-Online Learning for Robust and Adaptive Fault-Tolerance

Teirlinck, Casper (author)

Recent advancements in fault-tolerant flight control have involved model-free offline and online Reinforcement Learning algorithms in order to provide robust and adaptive control to autonomous systems. Inspired by recent work on Incremental Dual Heuristic Programming (IDHP) and Soft Actor-Critic (SAC), this research proposes a hybrid SAC-IDHP...

master thesis 2022

document

Pacing regulation for runners

Molano Valencia, Juan Esteban (author)

By increasing the step frequency of the runners, it is possible to reduce the risk of injuries due to overload. Techniques like auditory pacing help the athletes to have better control over their step frequency. Nevertheless, synchronizing to a continuous external rhythm costs energy. For this reason, the use of intermittent pacing may be more...

master thesis 2022