Continuous-Time Fitted Value Iteration for Robust Policies-Reference-Cited by-同舟云学术

Continuous-Time Fitted Value Iteration for Robust Policies

Published:2022 Issue: Volume: Page:1-15
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Lutter Michael¹^ORCID,Belousov Boris¹,Mannor Shie²,Fox Dieter³,Garg Animesh⁴^ORCID,Peters Jan¹^ORCID

Affiliation:

1. Intelligent Autonomous Systems group with the Computer Science Department, Technical University of Darmstadt, Darmstadt, Germany

2. The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, Technion-Israel Institute of Technology and a Distinguished Scientist at NVIDIA, Tel Aviv, Israel

3. Allen School of Computer Science & Engineering, University of Washington and Senior Director of Robotics Research at NVIDIA, Seattle, WA, USA

4. Computer Science Department, University of Toronto, Member of the Vector Institute and Senior Scientist at NVIDIA, Toronto, Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/4359286/09925102.pdf?arnumber=9925102

Reference94 articles.

1. Neural-Network-Based Online HJB Solution for Optimal Robust Guaranteed Cost Control of Continuous-Time Uncertain Nonlinear Systems

2. Domain randomization for simulation-based policy optimization with transferability assessment;muratore;Proc Conf Robot Learn,2018

3. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

4. Rl unplugged: Benchmarks for offline reinforcement learning;gulcehre,2020

5. Hamilton-Jacobi deep Q-Learning for deterministic continuous-time systems with lipschitz continuous controls;kim,2020

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