Hidden Parameter Recurrent State Space Models For Changing Dynamics
Scenarios

v1v2v3 (latest)

Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios

International Conference on Learning Representations (ICLR), 2022

29 June 2022

Gerhard Neumann

ArXiv (abs)PDF HTML

Papers citing "Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios"

6 / 6 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 354 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 186 0 0 02 Nov 2024
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty P. Becker Niklas Freymuth Gerhard Neumann Mamba 210 4 0 21 Jun 2024
Zero-Shot Reinforcement Learning via Function Encoders Tyler Ingebrand Amy Zhang Ufuk Topcu OffRL 357 12 0 30 Jan 2024
Multi Time Scale World ModelsNeural Information Processing Systems (NeurIPS), 2023 Vaisakh Shaj Saleh Gholam Zadeh Ozan Demir L. R. Douat Gerhard Neumann AI4CE 199 5 0 27 Oct 2023
Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion Simon Guist Jan Schneider Hao Ma Tianyu Cui V. Berenz ... Felix Gruninger M. Muhlebach J. Fiene Bernhard Schölkopf Le Chen 232 10 0 05 Jul 2023