CANOR COACH: Towards Noise-Robust Human-in-the-Loop Reinforcement Learning

• Author / Creator

  Li, Yuxuan

• Reinforcement learning has been widely applied in different control tasks.
  However, its performance often faces the challenge of low sample efficiency.
  Introducing human prior knowledge is often seen as a possible solution, such
  as behaviour cloning, learning from advice, and inverse reinforcement learning. Learning from feedback is an example of exploiting human knowledge
  and it is a method to enable the agent to learn from binary feedback, which
  describes the teacher’s attitude towards the agent’s action. Compared to traditional learning from demonstration methods, learning from feedback does
  not require expert-level knowledge. But this can also be a demerit as nonexpert feedback comes with inevitable noise. In this thesis, we investigate
  how and to which extent noise impacts the learning performance. We also
  propose a series of methods to de-noise the feedback data online and achieve
  noise-robust human-in-the-loop reinforcement learning with different amounts
  of prior knowledge.

• Subjects / Keywords

  • Reinforcement Learning
  • Machine Learning
  • Human-in-the-Loop Reinforcement Learning
  • Anomaly Detection

• Graduation date

  Fall 2024

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-5han-c270

• License

  This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.