Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities

Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to enable agents to learn and perform tasks autonomously with superhuman performance. However, we consider RL as fundamentally a Human-in-the-Loop (HITL) paradigm, even when an agent eventually performs its task autonomously. In cases where the reward function is challenging or impossible to define, HITL approaches are considered particularly advantageous. The application of Reinforcement Learning from Human Feedback (RLHF) in systems such as ChatGPT demonstrates the effectiveness of optimizing for user experience and integrating their feedback into the training loop. In HITL RL, human input is integrated during the…

• CI
  Canadian Institute for Advanced Research
• CC
  Compute Canada
• AM
  Alberta Machine Intelligence Institute
• UO
  University of Alberta
• AS
  Austrian Science Fund

  Award: P-32554
• M
  Mitacs
• NS
  Natural Sciences and Engineering Research Council of Canada