Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks

25 October 2021

Papers citing "Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks"

7 / 7 papers shown

Title
Deep Belief Markov Models for POMDP Inference Giacomo Arcieri K. Papakonstantinou D. Štraub Eleni Chatzi 43 0 0 17 Mar 2025
POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance Giacomo Arcieri C. Hoelzl Oliver Schwery D. Štraub K. Papakonstantinou Eleni Chatzi 13 13 0 16 Jul 2023
Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems Giacomo Arcieri C. Hoelzl Oliver Schwery D. Štraub K. Papakonstantinou Eleni Chatzi 14 21 0 15 Dec 2022
Deep Dynamics Models for Learning Dexterous Manipulation Anusha Nagabandi K. Konolige Sergey Levine Vikash Kumar 143 407 0 25 Sep 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 261 9,134 0 06 Jun 2015
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 251 7,633 0 03 Jul 2012