2022/06/27 by David Lindner, Lindner, David, Mennatallah El‐Assady +1 · 2 citations
Computer Science · Decision Sciences · Psychology · #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Innovation Diffusion and Forecasting #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mental Health Research Topics #Reinforcement Learning in Robotics
paper · pdf · doi:10.48550/arxiv.2206.13316
openalex publication_date 2022/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Reinforcement learning (RL) commonly assumes access to well-specified reward functions, which many practical applications do not provide. Instead, recently, more work has explored learning what to do from interacting with humans. So far, most of these approaches model humans as being (nosily) rational and, in particular, giving unbiased feedback. We argue that these models are too simplistic and that RL researchers need to develop more realistic human models to design and evaluate their algorithms. In particular, we argue that human models have to be personal, contextual, and dynamic. This paper calls for research from different disciplines to address key questions about how humans provide feedback to AIs and how we can build more robust human-in-the-loop RL systems.