PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning-Reference-Cited by-同舟云学术

PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning

Published:2024-03-11 Issue: Volume:32 Page:259-268
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Container-title:Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
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Author:

Holk Simon¹^ORCID,Marta Daniel¹^ORCID,Leite Iolanda¹^ORCID

Affiliation:

1. KTH Royal Institute of Technology, Stockholm, Sweden

Funder

Swedish Foundation for Strategic Research

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3610977.3634970

Reference78 articles.

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2. Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. 2016. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565 (2016).

3. Learning from Richer Human Guidance

4. Erik Båvenstrand and Jakob Berggren. 2019. Performance evaluation of imitation learning algorithms with human experts.

5. Kush Bhatia, Ashwin Pananjady, Peter Bartlett, Anca Dragan, and Martin J Wainwright. 2020. Preference learning along multiple criteria: A game-theoretic perspective. Advances in neural information processing systems , Vol. 33 (2020), 7413--7424.

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