On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning

17 October 2022

Papers citing "On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning"

8 / 8 papers shown

Title
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability Carlos E. Luis A. Bottero Julia Vinogradska Felix Berkenkamp Jan Peters 78 1 0 20 Feb 2025
Adaptive World Models: Learning Behaviors by Latent Imagination Under Non-Stationarity Emiliyan Gospodinov Vaisakh Shaj P. Becker Stefan Geyer Gerhard Neumann 34 0 0 02 Nov 2024
R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models Viet Dung Nguyen Zhizhuo Yang Christopher L. Buckley Alexander Ororbia 31 2 0 21 Sep 2024
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty P. Becker Niklas Freymuth Gerhard Neumann Mamba 26 2 0 21 Jun 2024
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving Daniel Bogdoll Lukas Bosch Tim Joseph Helen Gremmelmaier Yitian Yang J. Marius Zöllner 22 5 0 10 Aug 2023
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL P. Becker Sebastian Mossburger Fabian Otto Gerhard Neumann SSL 23 2 0 10 Feb 2023
Learning Dynamics Models for Model Predictive Agents M. Lutter Leonard Hasenclever Arunkumar Byravan Gabriel Dulac-Arnold Piotr Trochim N. Heess J. Merel Yuval Tassa AI4CE 57 26 0 29 Sep 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015