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Toward high-performance, memory-efficient, and fast reinforcement learning—Lessons from decision neuroscience

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Science Robotics  16 Jan 2019:
Vol. 4, Issue 26, eaav2975
DOI: 10.1126/scirobotics.aav2975

Article Information

vol. 4 no. 26

Published By: 
History: 
  • Received September 4, 2018
  • Accepted November 19, 2018

Author Information

  1. Jee Hang Lee1,2,*,
  2. Ben Seymour3,4,5,*,,
  3. Joel Z. Leibo6,
  4. Su Jin An1 and
  5. Sang Wan Lee1,2,7,
  1. 1Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
  2. 2KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea.
  3. 3Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.
  4. 4Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.
  5. 5Center for Information and Neural Networks, National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan.
  6. 6DeepMind, London, UK.
  7. 7KAIST Institute for Artificial Intelligence, Daejeon, Republic of Korea.
  1. Corresponding author. Email: bjs49{at}cam.ac.uk (B.S.); sangwan{at}kaist.ac.kr (S.W.L.)
  1. * These authors contributed equally to this work.

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