6 papers
On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference
Moritz A. Zanger, Yijun Wu, Pascal R. Van der Vaart +2
Uncertainty quantification is central to safe and efficient deployments of deep learning models, yet many computationally practical methods lack lacking rigorous theoretical motiva…
Sparse Masked Attention Policies for Reliable Generalization
Caroline Horsch, Laurens Engwegen, Max Weltevrede +2
In reinforcement learning, abstraction methods that remove unnecessary information from the observation are commonly used to learn policies which generalize better to unseen tasks.…
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…
How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning
Max Weltevrede, Moritz A. Zanger, Matthijs T. J. Spaan +1
In the zero-shot policy transfer setting in reinforcement learning, the goal is to train an agent on a fixed set of training environments so that it can generalise to similar, but…
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…
A Penalty-Based Guardrail Algorithm for Non-Decreasing Optimization with Inequality Constraints
Ksenija Stepanovic, Wendelin Böhmer, Mathijs de Weerdt
Traditional mathematical programming solvers require long computational times to solve constrained minimization problems of complex and large-scale physical systems. Therefore, the…