1 citations · 1 across the 4 of their papers we have counts for
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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…
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…