44 citations · 98 across the 11 of their papers we have counts for
16 papers
Herd Behavior in Decentralized Balancing Models: A Case Study in Belgium
Max Bruninx, Seyed Soroush Karimi Madahi, Timothy Verstraeten +3
In a decentralized balancing model, Balance Responsible Parties (BRPs) are encouraged by the Transmission System Operator (TSO) to deviate from their schedule to help the system re…
Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles
Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten +2
Accurate production forecasts are essential for the integration of renewable energy sources into the power grid. This paper illustrates how to obtain probabilistic forecasts of win…
Day-Ahead Bidding Strategies for Wind Farm Operators under a One-Price Balancing Scheme
Max Bruninx, Timothy Verstraeten, Jalal Kazempour +1
We study day-ahead bidding strategies for wind farm operators under a one-price balancing scheme, prevalent in European electricity markets. In this setting, the profit-maximising…
Evaluating COVID-19 vaccine allocation policies using Bayesian -top exploration
Alexandra Cimpean, Timothy Verstraeten, Lander Willem +3
Individual-based epidemiological models support the study of fine-grained preventive measures, such as tailored vaccine allocation policies, in silico. As individual-based models a…
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models
Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers +2
Many real-world problems contain multiple objectives and agents, where a trade-off exists between objectives. Key to solving such problems is to exploit sparse dependency structure…
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making
Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers +2
In many real-world scenarios, the utility of a user is derived from the single execution of a policy. In this case, to apply multi-objective reinforcement learning, the expected ut…
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Conor F. Hayes, Roxana Rădulescu, Eugenio Bargiacchi +15
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcemen…
Scalable Optimization for Wind Farm Control using Coordination Graphs
Timothy Verstraeten, Pieter-Jan Daems, Eugenio Bargiacchi +3
Wind farms are a crucial driver toward the generation of ecological and renewable energy. Due to their rapid increase in capacity, contemporary wind farms need to adhere to strict…
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
Roxana Rădulescu, Timothy Verstraeten, Yijie Zhang +3
Many real-world multi-agent interactions consider multiple distinct criteria, i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may le…
Deep reinforcement learning for large-scale epidemic control
Pieter Libin, Arno Moonens, Timothy Verstraeten +4
Epidemics of infectious diseases are an important threat to public health and global economies. Yet, the development of prevention strategies remains a challenging process, as epid…
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping
Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers +1
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allo…
Fleet Control using Coregionalized Gaussian Process Policy Iteration
Timothy Verstraeten, Pieter JK Libin, Ann Nowé
In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet…
Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures
Timothy Verstraeten, Eugenio Bargiacchi, Pieter JK Libin +3
Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this r…
IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers
Felipe Gomez Marulanda, Pieter Libin, Timothy Verstraeten +1
Over the last decade, the demand for better segmentation and classification algorithms in 3D spaces has significantly grown due to the popularity of new 3D sensor technologies and…
Fleetwide data-enabled reliability improvement of wind turbines
Timothy Verstraeten, Ann Nowe, Jonathan Keller +3
Wind farms are an indispensable driver toward renewable and nonpolluting energy resources. However, as ideal sites are limited, placement in remote and challenging locations result…
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies
Pieter Libin, Timothy Verstraeten, Diederik M. Roijers +4
Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies th…