3 papers
cs.LG2025
Priors Matter: Addressing Misspecification in Bayesian Deep Q-Learning
Pascal R. van der Vaart, Neil Yorke-Smith, Matthijs T. J. Spaan
Uncertainty quantification in reinforcement learning can greatly improve exploration and robustness. Approximate Bayesian approaches have recently been popularized to quantify unce…
cs.LG2025
Universal Value-Function Uncertainties
Moritz A. Zanger, Max Weltevrede, Yaniv Oren +4
Estimating epistemic uncertainty in value functions is a crucial challenge for many aspects of reinforcement learning (RL), including efficient exploration, safe decision-making, a…
cs.LG2025
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model
Moritz A. Zanger, Pascal R. Van der Vaart, Wendelin Böhmer +1
Uncertainty quantification is a critical aspect of reinforcement learning and deep learning, with numerous applications ranging from efficient exploration and stable offline reinfo…