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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202107811
|2 doi
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|a pubmed24n1110.xml
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|a (DE-627)NLM33329341X
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|a (NLM)34791712
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Lu, Yingming
|e verfasserin
|4 aut
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| 245 |
1 |
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|a In-Memory Realization of Eligibility Traces Based on Conductance Drift of Phase Change Memory for Energy-Efficient Reinforcement Learning
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 31.03.2022
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|a Date Revised 01.04.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2021 Wiley-VCH GmbH.
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|a Reinforcement learning (RL) has shown outstanding performance in handling complex tasks in recent years. Eligibility trace (ET), a fundamental and important mechanism in reinforcement learning, records critical states with attenuation and guides the update of policy, which plays a crucial role in accelerating the convergence of RL training. However, ET implementation on conventional digital computing hardware is energy hungry and restricted by the memory wall due to massive calculation of exponential decay functions. Here, in-memory realization of ET for energy-efficient reinforcement learning with outstanding performance in discrete- and continuous-state RL tasks is demonstrated. For the first time, the inherent conductance drift of phase change memory is exploited as physical decay function to realize in-memory eligibility trace, demonstrating excellent performance during RL training in various tasks. The spontaneous in-memory decay computing and storage of policy in the same phase change memory give rise to significantly enhanced energy efficiency compared with traditional graphics processing unit platforms. This work therefore provides a holistic energy and hardware efficient method for both training and inference of reinforcement learning
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|a Journal Article
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| 650 |
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4 |
|a conductance drift
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| 650 |
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4 |
|a eligibility trace
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| 650 |
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4 |
|a phase change memory
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| 650 |
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4 |
|a reinforcement learning
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| 700 |
1 |
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|a Li, Xi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Bonan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Longhao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Teng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Song, Zhitang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Ru
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Yuchao
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 6 vom: 04. Feb., Seite e2107811
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
1 |
8 |
|g volume:34
|g year:2022
|g number:6
|g day:04
|g month:02
|g pages:e2107811
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| 856 |
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|u http://dx.doi.org/10.1002/adma.202107811
|3 Volltext
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|d 34
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