Physics-Model-Regulated Deep Reinforcement Learning Towards Safety & Stability Guarantees-Reference-Cited by-同舟云学术

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Physics-Model-Regulated Deep Reinforcement Learning Towards Safety & Stability Guarantees

Published:2023-12-13 Issue: Volume: Page:
ISSN:
Container-title:2023 62nd IEEE Conference on Decision and Control (CDC)
language:
Short-container-title:

Author:

Cao Hongpeng¹,Mao Yanbing²,Sha Lui³,Caccamo Marco⁴

Affiliation:

1. Hongpeng Cao is with School of Engineering and Design, Technical University of Munich,Munich,Germany,85748

2. Wayne State University,Yanbing Mao is with Engineering Technology Division,Detroit,MI,USA,48201

3. University of Illinois at Urbana-Champaign,Department of Computer Science,Urbana,IL,USA,61801

4. School of Engineering and Design and Institute of Robotics and Machine Intelligence, Technical University of Munich,Munich,Germany

Funder

NSF

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10383192/10383193/10383560.pdf?arnumber=10383560

Reference25 articles.

1. End-to-end training of deep visuomotor policies;Levine;The Journal of Machine Learning Research,2016

2. RMA: Rapid Motor Adaptation for Legged Robots

3. Adversarial attacks on neural network policies;Huang,2017

4. Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

5. End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

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