6 papers
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…
Hierarchical Support Vector State Partitioning for Distilling Black Box Reinforcement Learning Policies
Senne Deproost, Mehrdad Asadi, Ann Nowé
We introduce State Vector Space Partitioning (SVSP), a novel method to mimic a black box reinforcement learning policy using a set of human-interpretable subpolicies. By partitioni…
Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning
Paolo Speziali, Arno De Greef, Mehrdad Asadi +3
We propose the Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO) for urban route planning for people with different accessibility requirements and preferences. With…
Explainable RL Policies by Distilling to Locally-Specialized Linear Policies with Voronoi State Partitioning
Senne Deproost, Dennis Steckelmacher, Ann Nowé
Deep Reinforcement Learning is one of the state-of-the-art methods for producing near-optimal system controllers. However, deep RL algorithms train a deep neural network, that lack…
Inclusive Fitness as a Key Step Towards More Advanced Social Behaviors in Multi-Agent Reinforcement Learning Settings
Andries Rosseau, Raphaël Avalos, Ann Nowé
The competitive and cooperative forces of natural selection have driven the evolution of intelligence for millions of years, culminating in nature's vast biodiversity and the compl…
Fairness-Aware Reinforcement Learning (FAReL): A Framework for Transparent and Balanced Sequential Decision-Making
Alexandra Cimpean, Nicole Orzan, Catholijn Jonker +2
Equity in real-world sequential decision problems can be enforced using fairness-aware methods. Therefore, we require algorithms that can make suitable and transparent trade-offs b…