1 citations · 1 across the 4 of their papers we have counts for
6 papers
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making
Axel Abels, Tom Lenaerts, Vito Trianni +1
Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory dec…
Distributional Multi-Objective Decision Making
Willem Röpke, Conor F. Hayes, Patrick Mannion +3
For effective decision support in scenarios with conflicting objectives, sets of potentially optimal solutions can be presented to the decision maker. We explore both what policies…
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees
Florent Delgrange, Ann Nowé, Guillermo A. Pérez
Although deep reinforcement learning (DRL) has many success stories, the large-scale deployment of policies learned through these advanced techniques in safety-critical scenarios i…
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
Raphael Avalos, Florent Delgrange, Ann Nowé +2
Partially Observable Markov Decision Processes (POMDPs) are used to model environments where the full state cannot be perceived by an agent. As such the agent needs to reason takin…
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem
Hélène Plisnier, Denis Steckelmacher, Jeroen Willems +2
Many instances of similar or almost-identical industrial machines or tools are often deployed at once, or in quick succession. For instance, a particular model of air compressor ma…
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…