Safe Reinforcement Learning in Uncertain Contexts

Author:

Baumann Dominik1ORCID,Schön Thomas B.2ORCID

Affiliation:

1. Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland

2. Department of Information Technology, Uppsala University, Uppsala, Sweden

Funder

Bundesministerium für Bildung und Forschung

Ministry of Culture and Science of the German State of North Rhine-Westphalia

Excellence Strategy of the Federal Government and the Länder, in part by the project New LEADS - New Directions in Learning Dynamical Systems

Vetenskapsrådet

Kjell och Märta Beijer Foundation

Mitsubishi Electric Research Laboratories

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Reference40 articles.

1. Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

2. Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics

3. Image2mass: Estimating the mass of an object from its image;Standley,2017

4. Explore the context: Optimal data collection for context-conditional dynamics models;Achterhold,2021

5. A kernel two-sample test;Gretton;J. Mach. Learn. Res.,2012

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3