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2023
Journal Article
Title

Guided Reinforcement Learning

Title Supplement
A Review and Evaluation for Efficient and effeective Real-World Robotics
Abstract
Recent successes aside, reinforcement learning (RL) still faces significant challenges in its application to the real-world robotics domain. Guiding the learning process with additional knowledge offers a potential solution, thus leveraging the strengths of data- and knowledge-driven approaches. However, this field of research encompasses several disciplines and hence would benefit from a structured overview.
Author(s)
Eßer, Julian
Fraunhofer-Institut für Materialfluss und Logistik IML  
Bach, Nicolas
Fraunhofer-Institut für Materialfluss und Logistik IML  
Jestel, Christian  
Fraunhofer-Institut für Materialfluss und Logistik IML  
Urbann, Oliver  
Fraunhofer-Institut für Materialfluss und Logistik IML  
Kerner, Sören  
Fraunhofer-Institut für Materialfluss und Logistik IML  
Journal
IEEE robotics & automation magazine  
Open Access
DOI
10.1109/MRA.2022.3207664
Additional full text version
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Language
English
Fraunhofer-Institut für Materialfluss und Logistik IML  
Keyword(s)
  • Robots

  • Task analysis

  • Pipelines

  • Training

  • Taxonomy

  • Automation

  • Computational modeling

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