Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement
Learning

Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning

3 June 2022

Bertrand Charpentier

Ransalu Senanayake

Mykel Kochenderfer

Stephan Günnemann

Papers citing "Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning"

6 / 6 papers shown

Title
Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning Daniel Flögel Marcos Gómez Villafane Joshua Ransiek Sören Hohmann 23 0 0 16 Sep 2024
Generalized Out-of-Distribution Detection: A Survey Jingkang Yang Kaiyang Zhou Yixuan Li Ziwei Liu 171 870 0 21 Oct 2021
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability Dibya Ghosh Jad Rahme Aviral Kumar Amy Zhang Ryan P. Adams Sergey Levine OffRL 270 107 0 13 Jul 2021
Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results Mohamad H. Danesh Alan Fern 94 14 0 11 Jul 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015