document

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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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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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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Learning-based path planning for automatic guided vehicles in container terminals: A case study at TBA Group

Wijnands, Patrick (author)

This thesis has provided insight into how machine learning can be beneficial to path planning in container terminals. Path planning algorithms can be used in environments with automated vehicles. A well known algorithm is the A* path planning algorithm, which is the fastest optimal path planning algorithm under satisfied conditions. However, the...

master thesis 2022

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Learning the Problem Representation for Improving Negotiation Strategies

Fledderus, Eddy (author)

The domains of the negotiation can vary significantly. It is possible that a domain is very cooperative, where both agents can receive a high utility; the opposite is also possible, where the domain is very competitive and the agents cannot both get a high utility. In the same manner, the agents can have different strategies leading to a...

bachelor thesis 2022

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Using Explainable Artificial Intelligence to Improve Transparency of Reinforcement Learning for Online Adaptive Flight Control: Breaking Open the Black Box

van Zijl, Job (author)

Deep Reinforcement Learning (DRL) shows great potential for flight control, due to its adaptability, fault-tolerance, and as it does not require an accurate system model. However, these techniques, like many machine learning applications, are considered black-box as their inner workings are hidden. This paper aims to break open the black box of...

master thesis 2022

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Using Decision Trees produced by Generative Adversarial Imitation Learning to give insight into black box Reinforcement Learning models

Meijer, Caspar (author)

Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...

bachelor thesis 2022

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Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems

Suau de Castro, M. (author), He, J. (author), Spaan, M.T.J. (author), Oliehoek, F.A. (author)

Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and the pace at which that data can be obtained. In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for...

conference paper 2022

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MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response

van Tilburg, Jasper (author)

Distribution System Operators (DSOs) are responsible preventing grid congestion, while accounting for growing demand and the intermittent nature of renewable energy resources. Incentive-based demand response programs promise real-time flexibility to relieve grid congestion. To include residential consumers in these programs, aggregators can...

master thesis 2021

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Reinforcement learning with domain-­specific relational inductive biases: Using Graph Neural Networks and domain knowledge

Vester, Erik (author)

Reinforcement Learning (RL) has been used to successfully train agents for many tasks, but generalizing to a different task - or even unseen examples of the same task - remains difficult. In this thesis, Deep Reinforcement Learning (DRL) is combined with Graph Neural Networks (GNNs) and domain knowledge, with the aim of improving the...

master thesis 2021

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Practical implementation of reinforcement learning algorithms for giving personalised speed advice to cyclists approaching intersections using function approximation and Dyna

Becker, Midas (author)

<br/>Being a safe and healthy alternative for polluting and space-inefficient motorised vehicles, cycling can strongly improve living conditions in urban areas. Idling in front of traffic lights is seen as one of the major inconveniences of commuting by bicycle. By giving personalised speed advice, the probability of catching a green light can...

master thesis 2021

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Optimal Control Strategies for Seasonal Thermal Energy Storage Systems with Market Interaction

Lago, Jesus (author), Suryanarayana, Gowri (author), Sogancioglu, Ecem (author), De Schutter, B.H.K. (author)

Seasonal thermal energy storage systems (STESSs) can shift the delivery of renewable energy sources and mitigate their uncertainty problems. However, to maximize the operational profit of STESSs and ensure their long-term profitability, control strategies that allow them to trade on wholesale electricity markets are required. While control...

journal article 2021

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A Looseness Detection Method for Railway Catenary Fasteners based on Reinforcement Learning Refined Localization

Zhong, Junping (author), Liu, Zhigang (author), Wang, H. (author), Liu, Wenqiang (author), Yang, Cheng (author), Han, Zhiwei (author), Nunez, Alfredo (author)

Brace sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too...

journal article 2021

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Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit

Ribeiro, M.J. (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)

Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in conflict detection and resolution (CD&amp;R) methods. The use of drones for applications such as package delivery, would result in traffic densities that are orders of magnitude higher than those currently...

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

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Knowing one’s opponents: Self Modeling Advantage Actor Critic for the Iterated Prisoner’s Dilemma

van der Toorn, Eric (author)

A recent advancement in Reinforcement Learning is the capability of modelling opponents. In this work, we are interested in going back to basics and testing this capability within the Iterated Prisoner's Dilemma, a simple method for modelling multi agent systems. Using the self modelling advantage actor critic model, we set up a single agent...

bachelor thesis 2020

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The Landing of a Quadcopter on Inclined Surfaces using Reinforcement Learning

Fris, Rein (author)

Deep Reinforcement Learning (DRL) enables us to design controllers for complex tasks with a deep learning approach. It allows us to design controllers that are otherwise cumbersome to design with conventional control methodologies. Often, an objective for RL is binary in nature. However, exploring in environments with sparse rewards is a problem...

master thesis 2020

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Reinforcement Learning for minimizing the total waiting time of passengers in a Taxi Dispatch Problem

Martens, Vera (author)

A Taxi Dispatch Problem involves assigning taxis to requests of passengers who are waiting at different locations for a trip. In today's economy and society, the Taxi Dispatch Problem and other transport problems can be found everywhere. Not only in transporting people, but also in food delivery from restaurants and package delivery for all kind...

bachelor thesis 2020

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Improvement of Conflict Detection and Resolution at High Densities Through Reinforcement Learning

Ribeiro, M.J. (author), Ellerbroek, J. (author), Hoekstra, J.M. (author)

The use of drones for applications such as package delivery, in an urban setting, would result in traffic densities that are orders of magnitude higher than any observed in manned aviation. Current geometric resolution models have proven to be very efficient. However, at the extreme densities envisioned for such drone applications, performance...

conference paper 2020

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Self-corrective Apprenticeship Learning for Quadrotor Control

Yuan, Haoran (author)

The control of aircraft can be carried out by Reinforcement Learning agents; however, the difficulty of obtaining sufficient training samples often makes this approach infeasible. Demonstrations can be used to facilitate the learning process, yet algorithms such as Apprenticeship Learning generally fail to produce a policy that outperforms the...

master thesis 2019