Self-Supervised Curriculum Generation for Autonomous Reinforcement Learning Without Task-Specific Knowledge-Reference-Cited by-同舟云学术

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Self-Supervised Curriculum Generation for Autonomous Reinforcement Learning Without Task-Specific Knowledge

Published:2024-05 Issue:5 Volume:9 Page:4043-4050
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Lee Sang-Hyun¹^ORCID,Seo Seung-Woo¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea

Funder

Institute of New Media and Communications

Institute of Engineering Research, Seoul National University

Automation and Systems at Seoul National University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7083369/10474437/10465617.pdf?arnumber=10465617

Reference21 articles.

1. End-to-end training of deep visuomotor policies;Levine;J. Mach. Learn. Res.,2016

2. Collective robot reinforcement learning with distributed asynchronous guided policy search

3. Learning dexterous in-hand manipulation

4. Scaling up multi-task robotic reinforcement learning;Kalashnikov,2022

5. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

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