5 papers
Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
Uncertainty estimation for Reinforcement Learning (RL) is a critical component in control tasks where agents must balance safe exploration and efficient learning. While deep neural…
Confidence Calibration in Large Language Model-Based Entity Matching
Iris Kamsteeg, Juan Cardenas-Cartagena, Floris van Beers +3
This research aims to explore the intersection of Large Language Models and confidence calibration in Entity Matching. To this end, we perform an empirical study to compare baselin…
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning
Aleksandar Todorov, Juan Cardenas-Cartagena, Rafael F. Cunha +2
Plasticity loss, a diminishing capacity to adapt as training progresses, is a critical challenge in deep reinforcement learning. We examine this issue in multi-task reinforcement l…
Upside-Down Reinforcement Learning for More Interpretable Optimal Control
Juan Cardenas-Cartagena, Massimiliano Falzari, Marco Zullich +1
Model-Free Reinforcement Learning (RL) algorithms either learn how to map states to expected rewards or search for policies that can maximize a certain performance function. Model-…
Forecasting Smog Clouds With Deep Learning
Valentijn Oldenburg, Juan Cardenas-Cartagena, Matias Valdenegro-Toro
In this proof-of-concept study, we conduct multivariate timeseries forecasting for the concentrations of nitrogen dioxide (NO2), ozone (O3), and (fine) particulate matter (PM10 & P…