Research ArticleARTIFICIAL INTELLIGENCE

A formal methods approach to interpretable reinforcement learning for robotic planning

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Science Robotics  18 Dec 2019:
Vol. 4, Issue 37, eaay6276
DOI: 10.1126/scirobotics.aay6276

Article Information

vol. 4 no. 37

DOI: 
https://doi.org/10.1126/scirobotics.aay6276
Published By: 
Science Robotics
History: 
  • Received July 5, 2019
  • Accepted November 4, 2019
  • .
Copyright & Usage: 
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works http://www.sciencemag.org/about/science-licenses-journal-article-reuseThis is an article distributed under the terms of the Science Journals Default License.

Author Information

  1. Xiao Li1,*,
  2. Zachary Serlin1,
  3. Guang Yang2 and
  4. Calin Belta1,2
  1. 1Department of Mechanical Engineering, Boston University, Boston, MA, USA.
  2. 2Division of Systems Engineering, Boston University, Boston, MA, USA.
  1. ↵*Corresponding author. Email: xli87{at}bu.edu

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