A Current-Mode Analog Circuit for Reinforcement Learning Problems

Abstract

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.