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A Current-Mode Analog Circuit for Reinforcement Learning Problems

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5 Author(s)
Terrence S. T. Mak ; Department of Systems Engineering & Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong ; K. P. Lam ; H. S. Ng ; G. Rachmuth
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Reinforcement learning is important for machine-intelligence and neurophysiological modelling applications to provide time-critical decision making. Analog circuit implementation has been demonstrated as a powerful computational platform for power-efficient, bio-implantable and real-time applications. This paper presents a current-mode analog circuit design for solving reinforcement learning problem with simple and efficient computational network architecture. The design has been fabricated and a new procedure to validate the fabricated reinforcement learning circuit will also be presented. This work provides a preliminary study for future biomedical application using CMOS VLSI reinforcement learning model.

Published in:

2007 IEEE International Symposium on Circuits and Systems

Date of Conference:

27-30 May 2007