Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

20 June 2017

Papers citing "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control"

33 / 33 papers shown

Title
DMWM: Dual-Mind World Model with Long-Term Imagination Lingyi Wang Rashed Shelim Walid Saad Naren Ramakrishnan LRM 175 1 0 11 Feb 2025
Increasing Information for Model Predictive Control with Semi-Markov Decision Processes Rémy Hosseinkhan Boucher Onofrio Semeraro L. Mathelin 48 0 0 28 Jan 2025
Residual Deep Gaussian Processes on Manifolds Kacper Wyrwal Andreas Krause Viacheslav Borovitskiy BDL 49 0 0 31 Oct 2024
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling Jasmine Bayrooti Carl Henrik Ek Amanda Prorok 42 0 0 07 Oct 2024
Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling Wenjun Huang Yunduan Cui Huiyun Li Xin Wu MU 22 0 0 20 Sep 2023
Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function Ruijie Zheng Xiyao Wang Huazhe Xu Furong Huang 48 13 0 02 Feb 2023
A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies David van de Sanden Maarten Schoukens Mauro Salazar 12 2 0 27 Nov 2022
Exploration via Planning for Information about the Optimal Trajectory Viraj Mehta I. Char J. Abbate R. Conlin M. Boyer Stefano Ermon J. Schneider W. Neiswanger OffRL 27 6 0 06 Oct 2022
FORESEE: Prediction with Expansion-Compression Unscented Transform for Online Policy Optimization Hardik Parwana Dimitra Panagou 25 2 0 26 Sep 2022
Variational Inference for Model-Free and Model-Based Reinforcement Learning Felix Leibfried OffRL 13 0 0 04 Sep 2022
Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models Zhongyu Li Jun Zeng A. Thirugnanam K. Sreenath 26 16 0 11 May 2022
Gradient-Based Trajectory Optimization With Learned Dynamics Bhavya Sukhija Nathanael Kohler Miguel Zamora Simon Zimmermann Sebastian Curi Andreas Krause Stelian Coros 30 9 0 09 Apr 2022
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation Aivar Sootla Alexander I. Cowen-Rivers Taher Jafferjee Ziyan Wang D. Mguni Jun Wang Haitham Bou-Ammar 32 54 0 14 Feb 2022
Improving Hyperparameter Optimization by Planning Ahead H. Jomaa Jonas K. Falkner Lars Schmidt-Thieme 22 0 0 15 Oct 2021
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts Weinan Zhang Xihuai Wang Jian Shen Ming Zhou 27 35 0 07 May 2021
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow John Mcleod Hrvoje Stojić Vincent Adam Dongho Kim Jordi Grau-Moya Peter Vrancx Felix Leibfried OffRL 21 2 0 26 Mar 2021
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning Sebastian Curi Ilija Bogunovic Andreas Krause 39 17 0 18 Mar 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 42 49 0 27 Dec 2020
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling Ying Fan Yifei Ming 25 17 0 20 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 57 0 08 Nov 2020
An Iterative LQR Controller for Off-Road and On-Road Vehicles using a Neural Network Dynamics Model Akhil Nagariya Srikanth Saripalli 19 28 0 28 Jul 2020
Anticipating the Long-Term Effect of Online Learning in Control A. Capone Sandra Hirche 19 4 0 24 Jul 2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning Sebastian Curi Felix Berkenkamp Andreas Krause 30 82 0 15 Jun 2020
SAMBA: Safe Model-Based & Active Reinforcement Learning Alexander I. Cowen-Rivers Daniel Palenicek Vincent Moens Mohammed Abdullah Aivar Sootla Jun Wang Haitham Bou-Ammar 21 44 0 12 Jun 2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data Marc Finzi Samuel Stanton Pavel Izmailov A. Wilson 28 316 0 25 Feb 2020
On Simulation and Trajectory Prediction with Gaussian Process Dynamics Lukas Hewing Elena Arcari Lukas P. Frohlich M. Zeilinger 17 35 0 23 Dec 2019
Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization Sumeet Singh Spencer M. Richards Vikas Sindhwani Jean-Jacques E. Slotine Marco Pavone 29 74 0 29 Jul 2019
Learning to Guide: Guidance Law Based on Deep Meta-learning and Model Predictive Path Integral Control Chen Liang Weihong Wang Zhenghua Liu Chao Lai Benchun Zhou 16 28 0 15 Apr 2019
RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems E. Abramova Luke Dickens Daniel Kuhn A. Faisal 16 3 0 07 Mar 2019
VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control Xingxing Liang Qi Wang Yanghe Feng Zhong Liu Jincai Huang 21 5 0 24 Dec 2018
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems K. P. Wabersich M. Zeilinger AI4CE 20 154 0 13 Dec 2018
Model-Based Reinforcement Learning via Meta-Policy Optimization I. Clavera Jonas Rothfuss John Schulman Yasuhiro Fujita Tamim Asfour Pieter Abbeel 19 224 0 14 Sep 2018
Meta Reinforcement Learning with Latent Variable Gaussian Processes Steindór Sæmundsson Katja Hofmann M. Deisenroth BDL OffRL AI4CE 8 142 0 20 Mar 2018