Active Reward Learning from Online Preferences
Author:
Affiliation:
1. Stanford University,Computer Science
2. Stanford University,Electrical Engineering
Funder
NSF
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10160211/10160212/10160439.pdf?arnumber=10160439
Reference59 articles.
1. Active Preference-Based Gaussian Process Regression for Reward Learning
2. Babyai 1.1;hui;ArXiv Preprint,2020
3. ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
4. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning;yu;Conference on Robot Learning,2020
5. Asking easy questions: A user-friendly approach to active reward learning;biyik;Proceedings of the 3rd Conference on Robot Learning (CoRL),0
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1. Batch Active Learning of Reward Functions from Human Preferences;ACM Transactions on Human-Robot Interaction;2024-06-14
2. Active preference-based Gaussian process regression for reward learning and optimization;The International Journal of Robotics Research;2023-11-07
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