Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework

Open access
Date
2024-11-18Type
- Working Paper
ETH Bibliography
yes
Altmetrics
Abstract
Reinforcement Learning from Human feedback (RLHF) has become a powerful tool to fine-tune or train agentic machine learning models. Similar to how humans interact in social contexts, we can use many types of feedback to communicate our preferences, intentions, and knowledge to an RL agent. However, applications of human feedback in RL are often limited in scope and disregard human factors. In this work, we bridge the gap between machine learning and human-computer interaction efforts by developing a shared understanding of human feedback in interactive learning scenarios. We first introduce a taxonomy of feedback types for reward-based learning from human feedback based on nine key dimensions. Our taxonomy allows for unifying human-centered, interface-centered, and model-centered aspects. In addition, we identify seven quality metrics of human feedback influencing both the
human ability to express feedback and the agent’s ability to learn from the feedback. Based on the feedback taxonomy and quality criteria, we derive requirements and design choices for systems learning from human feedback. We relate these requirements and design choices to existing work in interactive machine learning. In the process, we identify gaps in existing work and future research opportunities. We call for interdisciplinary collaboration to harness the full potential of reinforcement learning with data-driven co-adaptive modeling and varied interaction mechanics. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000717817Publication status
publishedExternal links
Journal / series
arXivPublisher
Cornell UniversityEdition / version
v1Subject
reinforcement learning from human feedback; RLHF; Framework; Human feedback; Human centered computingOrganisational unit
09822 - El-Assady, Mennatallah / El-Assady, Mennatallah
09822 - El-Assady, Mennatallah / El-Assady, Mennatallah
More
Show all metadata
ETH Bibliography
yes
Altmetrics