A formal methods approach to interpretable reinforcement learning for robotic planning-Reference-Cited by-同舟云学术

A formal methods approach to interpretable reinforcement learning for robotic planning

Published:2019-12-18 Issue:37 Volume:4 Page:
ISSN:2470-9476
Container-title:Science Robotics
language:en
Short-container-title:Sci. Robot.

Author:

Li Xiao¹^ORCID,Serlin Zachary¹^ORCID,Yang Guang²,Belta Calin¹²

Affiliation:

1. Department of Mechanical Engineering, Boston University, Boston, MA, USA.

2. Division of Systems Engineering, Boston University, Boston, MA, USA.

Abstract

A formal methods approach to reinforcement learning generates rewards from a formal language and guarantees safety.

Funder

National Science Foundation

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Mechanical Engineering

Reference46 articles.

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3. A. Y. Ng D. Harada S. J. Russell Policy invariance under reward transformations: Theory and application to reward shaping in Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999) pp. 278–287.

4. P. F. Christiano M. Abate D. Amodei Supervising strong learners by amplifying weak experts. arXiv:1810.08575 [cs.LG] (2018).

5. D. Hadfield-Menell S. J. Russell P. Abbeel A. Dragan Cooperative inverse reinforcement learning in Proceedings of Advances in Neural Information Processing Systems (NeurIPS 2016) pp. 3909–3917.

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