文章基本信息

标题：A Low-Power Actor-Critic Framework Based on Memristive Spiking Neural Network
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作者：Yaozhong Zhang ; Xiaofang Hu ; Yue Zhou 等
期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：3
页码：1-9
DOI：10.1088/1755-1315/252/3/032157
出版社：IOP Publishing
摘要：Traditional deep reinforcement learning (DRL) algorithms consume much energy. Energy-efficient spiking neural networks (SNNs) are promising technologies to bulid a low-power reinforcement learning architecture. In this paper, an actor-critic framework based on memrisitive SNN is proposed. To convey and process information in SNN, spike encoding and decoding systems are created. Then, an improved learning algorithm based on spike-timing-dependent plasticity (STDP) learning rule is designed to combine actor-critic method with SNN. Moreover, this learning algorithm is also hardware-friendly. Besides, memristive synapse is designed to accelerate this learning algorithm. Finally, a continuous control problem is applied to illustrate the effectiveness of the proposed framework. The results show the proposed framework is prior to traditional methods.