APReL: A Library for Active Preference-based Reward Learning Algorithms-Reference-Cited by-同舟云学术

APReL: A Library for Active Preference-based Reward Learning Algorithms

Published:2022-03-07 Issue: Volume: Page:
ISSN:
Container-title:2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
language:
Short-container-title:

Author:

Biyik Erdem¹,Talati Aditi²,Sadigh Dorsa³

Affiliation:

1. Stanford University,Electrical Engineering

2. Stanford University,Computer Science

3. Stanford University,Computer Science & Electrical Engineering

Funder

NSF

FLI

DARPA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9889327/9889305/09889650.pdf?arnumber=9889650

Reference34 articles.

1. A simple and fast algorithm for K-medoids clustering

2. An Active Learning Algorithm for Ranking from Pairwise Preferences with an Almost Optimal Query Complexity;ailon;Journal of Machine Learning Research,2012

3. Here's What I've Learned: Asking Questions that Reveal Re-ward Learning;habibian;ArXiv Preprint,2021

4. Preference-based Learning of Reward Function Features;katz;ArXiv Preprint,2021

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Batch Active Learning of Reward Functions from Human Preferences;ACM Transactions on Human-Robot Interaction;2024-06-14

2. Designing Reward Functions Using Active Preference Learning for Reinforcement Learning in Autonomous Driving Navigation;Applied Sciences;2024-06-03

3. Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

4. REACT: Two Datasets for Analyzing Both Human Reactions and Evaluative Feedback to Robots Over Time;Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction;2024-03-11

5. Leveraging Implicit Human Feedback to Better Learn from Explicit Human Feedback in Human-Robot Interactions;Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction;2024-03-11