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

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Improved DQN-Based Computation Offloading Algorithm in MEC Environment

Zhao, Zheyu (author), Cheng, H. (author), Xu, Xiaohua (author)

Massive terminal users have brought explosive need of data residing at edge of overall network. Multiple Mobile Edge Computing (MEC) servers are built in/near base station to meet this need. However, optimal distribution of these servers to multiple users in real time is still a problem. Reinforcement Learning (RL) as a framework to solve...

conference paper 2023

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DMQL: Deep Maximum Q-Learning: Combatting Relative Overgeneralisation in Deep Independent Learners using Optimism and Similarity

Dam, Erwin (author)

Various pathologies can occur when independent learners are used in cooperative Multi-Agent Reinforcement Learning. One such pathology is Relative Overgeneralisation, which manifests when a suboptimal Nash Equilibrium in the joint action space of a problem is preferred over an optimal Equilibrium. Approaches exist to combat relative...

master thesis 2022

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Sequential decision making using Bayesian networks and reinforcement learning: to determine the hydrocarbon prospect exploration strategy

Veerhoek, Laura (author)

In the process of drilling wells to produce hydrocarbons, an exploration strategy is used to determine which wells should be drilled and in which order. This strategy is vital, as a suboptimal drilling sequence will lead to more expenses and fewer gains.<br/>Furthermore, the wells considered in most exploration strategies are geologically<br/...

master thesis 2022

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The Feasibility of Reinforcement Learning as a replacement for Mixed-Integer Programming for Smart Charging of Electric Vehicles

Becker, Simion (author)

As the world is currently actively trying to reduce the consumption of fossil fuels, large investments are done in renewable energy sources and ways are sought after to electrify fossil fuel-intensive sectors. In line with these developments, the number of electric vehicles requiring access to the electric power grid has exploded putting...

master thesis 2022

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Predictive Maintenance Decisions for a Multi-Component Aircraft based on Prognostics

Posthuma, Luuk (author)

Aircraft maintenance is critical to an airline's operations to ensure the reliability, availability, and safety of their assets. Recently, the approach of using component prognostics in aircraft maintenance has received increasing attention in academic- and industrial research. Predictive maintenance has demonstrated promising results in using...

master thesis 2022

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Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

Stepanovic, K. (author), Wu, J. (author), Everhardt, Rob (author), de Weerdt, M.M. (author)

abstract 2022

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Global synchromodal shipment matching problem with dynamic and stochastic travel times: a reinforcement learning approach

Guo, W. (author), Atasoy, B. (author), Negenborn, R.R. (author)

Global synchromodal transportation involves the movement of container shipments between inland terminals located in different continents using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of accepted shipments with...

journal article 2022

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Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

Stepanovic, K. (author), Wu, J. (author), Everhardt, Rob (author), de Weerdt, M.M. (author)

The integration of pipeline energy storage in the control of a district heating system can lead to profit gain, for example by adjusting the electricity production of a combined heat and power (CHP) unit to the fluctuating electricity price. The uncertainty from the environment, the computational complexity of an accurate model, and the scarcity...

journal article 2022

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Event-Based Communication in Distributed Q-Learning

Jarne Ornia, D. (author), Mazo, M. (author)

We present an approach to reduce the communication of information needed on a Distributed Q-Learning system inspired by Event Triggered Control (ETC) techniques. We consider a baseline scenario of a Distributed Q-Learning problem on a Markov Decision Process (MDP). Following an event-based approach, N agents sharing a value function explore the...

conference paper 2022

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Simulating an Scheduling Airport Security Checkpoints: Q-Learning-Based Allocation of Operators to Security Teams at an Airport Security Checkpoint

Köstler, Klemens (author)

In this paper, we propose and analyze a q-learning-based approach for allocation of operators to security teams in order to improve operational efficiency of an airport security checkpoint. The research is composed of two parts. First, we develop an agent-based model capable of simulating an airport security checkpoint. Second, we introduce...

master thesis 2021

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Deep Reinforcement Learning for Rapid Communication Network Recovery with Multiple Repair Crews

Vilhjálmsson, Ingimundur (author)

Natural disasters can destroy communication network components, potentially leading to severe losses in connectivity. During those devastating events, network connectivity is crucial for rescue teams as well as anyone in need of assistance. Therefore, swift network restoration following a disaster is vital. However, post-disaster network...

master thesis 2021

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Reinforcement learning for order distribution in self-organizing logistics

de Vries, Yorick (author)

With the increasing global demand for logistics, supply chains have grown a lot in volume over the last decades. To be able to operate effectively within the capacity constraints of the carriers, proper collaboration and optimization of order allocation is required. Van Berkel Logistics facilitates the transport of containers by trucks from sea...

master thesis 2021

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Comparing Model-Free Deep Reinforcement Learning Algorithms on Stock Market

Meral, Murat Kaan Meral (author)

Automated asset trading is a crucial method used by financial entities such as investment firms or hedge funds. It allows them to allocate their capital in order to maximize their rate of returns. In scientific literature, there are multiple models suggested to solve this problem. However, these models either lack the complexity to understand...

bachelor thesis 2021

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Comparing Deep Reinforcement Learning Approaches for Sparse Reward Settings with Discrete State-Action Spaces

Çapanoğlu, Alp (author)

One of the most challenging types of environments for a Deep Reinforcement Learning agent to learn in are those with sparse reward functions. There exist algorithms that are designed to perform well in settings with sparse rewards, but they are often applied to continuous state-action spaces, since economically relevant problems like robotic...

bachelor thesis 2021

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Safe Reinforcement Learning by Shielding for Autonomous Vehicles

Zoon, Job (author)

In the past few years, there has been much research in the field of Autonomous Vehicles (AV). If AVs are implemented in our daily lives, this could have many advantages. Before this can happen, safe driver models need to be designed which control the AVs. One technique that is suitable to create these models is Reinforcement Learning (RL). A...

master thesis 2021

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Towards Explainable Automation for Air Traffic Control Using Deep Q-learning from Demonstrations and Reward Decomposition

Hermans, Max (author)

The current ATC system is seen as the most significant limitation to coping with an increased air traffic density. Transitioning towards an ATC system with a high degree of automation is essential to cope with future traffic demand of the airspace. In recent studies, reinforcement learning has shown promising results automating Conflict...

master thesis 2021

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Safe Curriculum Learning for Linear Systems With Unknown Dynamics in Primary Flight Control

De Buysscher, Diego (author)

Safe Curriculum Learning constitutes a collection of methods that aim at enabling Rein- forcement Learning (RL) algorithms on complex systems and tasks whilst considering the safety and efficiency aspect of the learning process. On the one hand, curricular reinforce- ment learning approaches divide the task into more gradual complexity stages to...

master thesis 2021

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Aircraft maintenance check scheduling using reinforcement learning

Andrade, Pedro (author), Silva, Catarina (author), Ribeiro, Bernardete (author), Santos, Bruno F. (author)

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is...

journal article 2021

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Reinforcement Learning for Profiled Side-Channel Analysis: Applications of Q-Learning in the SCA Domain

Rijsdijk, Jorai (author)

Side-channel attacks (SCA), which use unintended leakage to retrieve a secret cryptographic key, have become more sophisticated over time. With the recent successes of machine learning (ML) and especially deep learning (DL) techniques against cryptographic implementations even in the presence of dedicated countermeasures, various methods have...

master thesis 2020