Improving the Robustness of Reinforcement Learning Policies With ${\mathcal {L}_{1}}$ Adaptive Control-Reference-Cited by-同舟云学术

Improving the Robustness of Reinforcement Learning Policies With ${\mathcal {L}_{1}}$ Adaptive Control

Published:2022-07 Issue:3 Volume:7 Page:6574-6581
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Cheng Yikun¹^ORCID,Zhao Pan¹^ORCID,Wang Fanxin¹^ORCID,Block Daniel J.²,Hovakimyan Naira¹^ORCID

Affiliation:

1. Mechanical Science and Engineering Department, University of Illinois at Urbana-Champaign, IL, USA

2. Electrical and Computer Engineering Department, University of Illinois at Urbana-Champaign, IL, USA

Funder

Air Force Office of Scientific Research

NSF

AI Institute Planning

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

https://ieeexplore.ieee.org/ielam/7083369/9750005/9761728-aam.pdf

Reference35 articles.

1. Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics

2. Improving the robustness of reinforcement learning policies with $\mathcal {L}_{1}$ adaptive control;cheng,2021

3. Robust adaptive control of linear parameter-varying systems with unmatched uncertainties;zhao,0

4. Adaptive Robust Quadratic Programs using Control Lyapunov and Barrier Functions

5. Probabilistic differential dynamic programming;pan;Proc Adv Neural Inf Process Syst,0

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