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

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.

  • Published in:
    IEEE Robotics & Automation Magazine
  • Type:
    Article
  • Authors:
    J. Eßer, N. Bach, C. Jestel, O. Urbann, S. Kerner
  • 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, Esser.etal.2022,