Reinforcement Learning in Memristive Spiking Neural Networks through Modulation of ReSuMe

被引：2

作者：

Ji, Xun ^{[1
]}

Zhang, Yaozhong ^{[1
]}

Li, Chuxi ^{[1
]}

Wu, Tanghong ^{[1
]}

Hu, Xiaofang ^{[1
]}

机构：

[1] Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China

来源：

ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS III | 2019年 / 2073卷

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Reinforcement Learning; Spiking Neural Network; Remote Supervised Method; Memristor;

D O I：

10.1063/1.5090748

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, a novel hardware-friendly reinforcement learning algorithm based on memristive spiking neural networks (MSNN-RL) is proposed. Neurons for spike coding are designed specifically to complete transformation between analog data and discrete spikes. Then, remote supervised method (ReSuMe) is used to combine SNN with basic reforcement learing (Sarsa). Besides, bionic memristive snynapses are designed to speed up ReSuMe. Furthermore, the circuit scheme of MSNN-RL is designed with modulation of memristor synapses. Finally, the application of MSNN-RL in acrobot system is discussed. Simulation results and analysis verify the effectiveness of the proposed algorithm (MSNN-RL) and show it is superior to traditional apporach.

引用

页数：9

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