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Guided Reinforcement Learning: A Review and Evaluation for Efficient and Effective Real-World Robotics

Author: J. Eßer, N. Bach, C. Jestel, O. Urbann, S. Kerner
Journal: IEEE Robotics & Automation Magazine
Year: 2022

Citation information

J. Eßer, N. Bach, C. Jestel, O. Urbann, S. Kerner:
Guided Reinforcement Learning: A Review and Evaluation for Efficient and Effective Real-World Robotics.
IEEE Robotics & Automation Magazine,
2022,
2-22,
https://doi.org/10.1109/MRA.2022.3207664

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.