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Numerical simulation of deep reinforcement learning using self-referential holographic deep neural network
http://hdl.handle.net/10228/0002001766
http://hdl.handle.net/10228/0002001766ad2ee200-b567-46e8-9b57-180a7c71bb88
| 名前 / ファイル | ライセンス | アクション |
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neuro_110.pdf (333.7 KB)
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| Item type | 共通アイテムタイプ(1) | |||||||||||
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| 公開日 | 2025-07-11 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Numerical simulation of deep reinforcement learning using self-referential holographic deep neural network | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
Tomioka, Rio
× Tomioka, Rio
× 高林, 正典
WEKO
35482
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Deep reinforcement learning (DRL) is an artificial intelligence that is capable of autonomous decision-making in complex situations through a complementary combination of deep neural networks (DNNs) and reinforcement learning (RL). The autonomy is based on the cooperation between RL, which explores adaptations through trial and error in the environment; database (DB), which stores experience; and DNN, which learns adaptive behavior from training on experience data. The cooperation has been successfully applied to tasks that must operate directly in the face of a complex world, such as robotics, Go AI, and data center cooling. Regarding DNNs, the advancement of applications and algorithms requires the improvement of energy efficiency. There are two approaches to address this issue. The first is based on the current mainstream of electronic digital computing, such as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs). The second explores new computing paradigms not dependent on von Neumann architectures, such as the search for extreme performance using optical computing. Although ASIC- and FPGA-based approaches have been studied for DNNs used in DRLs named q-networks [1,2], to the best of the authors' knowledge, there is no research on non-von Neumann computing. Here, we focus on self-referential holography (SRH) [3], an optoelectronic technology that implements two functions, as large-scale DB and DNNs exploiting the spatial parallelism of light, in a single system. In this study, we numerically investigate the feasibility of q-networks based on self-referential holographic DNN (SR-HDNN) [4]. | |||||||||||
| 言語 | en | |||||||||||
| 備考 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | International Workshop on Holography and Related Technologies 2024 (IWH 2024) , December 10-12, 2024, Hawaii, USA | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : International Workshop on Holography and Related Technologies 2024 (IWH 2024) Technical Digest 発行日 2024-12 |
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| 出版社 | ||||||||||||
| 出版者 | 日本光学会 | |||||||||||
| 言語 | ja | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||||
| 資源タイプ | conference paper | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| 会議記述 | ||||||||||||
| 会議名 | International Workshop on Holography and Related Technologies 2024 (IWH 2024) | |||||||||||
| 言語 | en | |||||||||||
| 開始年 | 2024 | |||||||||||
| 開始月 | 12 | |||||||||||
| 開始日 | 10 | |||||||||||
| 終了年 | 2024 | |||||||||||
| 終了月 | 12 | |||||||||||
| 終了日 | 12 | |||||||||||
| 開催地 | Hawaii | |||||||||||
| 言語 | en | |||||||||||
| 開催国 | USA | |||||||||||
| 研究者情報 | ||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000508_ja.html | |||||||||||
| 連携ID | ||||||||||||
| 値 | 14665 | |||||||||||
