document

Autonomous greenhouse climate control with Q-learning using ENMPC as a function approximator

Lubbers, Seymour (author)

Greenhouses allow production of crops that would otherwise be impossible. Permitting more local, fresher and nutrient richer crop production. Eorts are taken to minimize societal harm due to energy and resource consumption by greenhouse production systems. One way to control such systems is by using model predictive control. Optimal crop yield...

master thesis 2023

document

Safe & Intelligent Control: Fault-tolerant Flight Control with Distributional and Hybrid Reinforcement Learning using DSAC and IDHP

Vieira dos Santos, Lucas (author)

The critical challenge for employing autonomous control systems in aircraft is ensuring robustness and safety. This study introduces an intelligent and fault-tolerant controller that merges two Reinforcement Learning (RL) algorithms in a hybrid approach: the Distributional Soft Actor-Critic (DSAC) and the Incremental Dual Heuristic Programming ...

master thesis 2023

document

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

document

A Method for Embodied Co-Learning in Interdependent Human-Robot Teams

Loopik, Hugo (author)

This paper addresses the research question: “How can a human-robot team achieve co-learning, and interdependence in physically embodied tasks?”<br/>A method has been developed that enables a human-robot team to co-learn the handover of an object from the robot to the human. Five design requirements were composed to address the challenges of...

master thesis 2023

document

Graph convolution reinforcement learning for active wake control in windfarms: Application of a multi-agent reinforcement learning algorithm

Yeh, Jefferson (author)

Wind energy, generated by windfarms, is playing an increasingly critical role in meeting current and future energy demands. windfarms, however, face a challenge due to the inherent flaw of wake-induced power losses when turbines are located in close proximity. Wakes, characterized by regions of turbulence and lower wind speed, are created as air...

bachelor thesis 2023

document

Effects of action space discretization and DQN extensions on algorithm robustness and efficiency: How do the discretization of the action space and various extensions to the well-known DQN algorithm influence training and the robustness of final policies under various testing conditions?

Sözüdüz, Mehmet (author)

Reinforcement Learning (RL) has gained atten-tion as a way of creating autonomous agents for self-driving cars. This paper explores the adap- tation of the Deep Q Network (DQN), a popular deep RL algorithm, in the Carla traffic simulator for autonomous driving. It investigates the influ- ence of action space discretization and DQN ex-<br/...

bachelor thesis 2023

document

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

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

document

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

document

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

document

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

document

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

document

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

document

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

document

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

document

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

document

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

document

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

document

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

document

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