extended-abstract

Uncertainties based queries for Interactive policy learning with evaluations and corrections

Authors:

Carlos Celemin,

Jens KoberAuthors Info & Claims

ICMI '21 Companion: Companion Publication of the 2021 International Conference on Multimodal Interaction

Pages 192 - 193

https://doi.org/10.1145/3461615.3485404

Published: 17 December 2021 Publication History

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References

[1]

Riad Akrour, Marc Schoenauer, and Michele Sebag. 2011. Preference-based policy learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 12–27.

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Sonia Chernova and Manuela Veloso. 2009. Interactive policy learning through confidence-based autonomy. Journal of Artificial Intelligence Research 34 (2009), 1–25.

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Paul Christiano, Jan Leike, Tom B Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep reinforcement learning from human preferences. arXiv preprint arXiv:1706.03741(2017).

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Ryan Hoque, Ashwin Balakrishna, Carl Putterman, Michael Luo, Daniel S Brown, Daniel Seita, Brijen Thananjeyan, Ellen Novoseller, and Ken Goldberg. 2021. LazyDAgger: Reducing Context Switching in Interactive Imitation Learning. arXiv preprint arXiv:2104.00053(2021).

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Michael Kelly, Chelsea Sidrane, Katherine Driggs-Campbell, and Mykel J Kochenderfer. 2019. Hg-dagger: Interactive imitation learning with human experts. In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 8077–8083.

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James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, Guan Wang, David L Roberts, Matthew E Taylor, and Michael L Littman. 2017. Interactive learning from policy-dependent human feedback. In International Conference on Machine Learning. PMLR, 2285–2294.

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Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del Solar, and Jens Kober. 2018. Interactive learning with corrective feedback for policies based on deep neural networks. In International Symposium on Experimental Robotics. Springer, 353–363.

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Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, and Peter Stone. 2018. Deep tamer: Interactive agent shaping in high-dimensional state spaces. In Thirty-Second AAAI Conference on Artificial Intelligence.

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Uncertainties based queries for Interactive policy learning with evaluations and corrections

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Published In

ICMI '21 Companion: Companion Publication of the 2021 International Conference on Multimodal Interaction

October 2021

418 pages

ISBN:9781450384711

DOI:10.1145/3461615

Editors:
Zakia Hammal
Carnegie Mellon University
,
Carlos Busso
University of Texas at Dallas
,
Catherine Pelachaud
CNRS - ISIR, Sorbonne University
,
Sharon Oviatt
Monash University
,
Albert Ali Salah
Utrecht University and Boğaziçi University
,
Guoying Zhao
University of Oulu

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 17 December 2021

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ICMI '21: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 18 - 22, 2021

QC, Montreal, Canada

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Interactive Learning of Spoken Words and Their Meanings Through an Audio-Visual Interface

Interactive relational reinforcement learning of concept semantics

Simple and fast algorithms for interactive machine learning with random counter-examples

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