Toward Optimized In‐Memory Reinforcement Learning: Leveraging 1/f Noise of Synaptic Ferroelectric Field‐Effect‐Transistors for Efficient Exploration

Reinforcement learning (RL), exhibiting outstanding performance in various fields, requires large amounts of data for high performance. While exploration techniques address this requirement, conventional exploration methods have limitations: complexity of hardware implementation and significant hard...

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Bibliografische Detailangaben

Hauptverfasser:	Jangsaeng Kim, Wonjun Shin, Jiyong Yim, Dongseok Kwon, Daewoong Kwon, Jong‐Ho Lee
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Wiley 2024-06-01
Schriftenreihe:	Advanced Intelligent Systems
Schlagworte:	computing‐in‐memory exploration ferroelectric field‐effect‐transistors low‐frequency noise reinforcement learning
Online-Zugang:	https://doi.org/10.1002/aisy.202300763
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