Controlling chaotic itinerancy in laser dynamics for reinforcement learning Article Swipe
Ryugo Iwami
,
Takatomo Mihana
,
Kazutaka Kanno
,
Satoshi Sunada
,
Makoto Naruse
,
Atsushi Uchida
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.05987
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.05987
Photonic artificial intelligence has attracted considerable interest in accelerating machine learning; however, the unique optical properties have not been fully utilized for achieving higher-order functionalities. Chaotic itinerancy, with its spontaneous transient dynamics among multiple quasi-attractors, can be employed to realize brain-like functionalities. In this paper, we propose a method for controlling the chaotic itinerancy in a multi-mode semiconductor laser to solve a machine learning task, known as the multi-armed bandit problem, which is fundamental to reinforcement learning. The proposed method utilizes ultrafast chaotic itinerant motion in mode competition dynamics controlled via optical injection. We found that the exploration mechanism is completely different from a conventional searching algorithm and is highly scalable, outperforming the conventional approaches for large-scale bandit problems. This study paves the way to utilize chaotic itinerancy for effectively solving complex machine learning tasks as photonic hardware accelerators.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2205.05987
- https://arxiv.org/pdf/2205.05987
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280518431
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https://openalex.org/W4280518431Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2205.05987Digital Object Identifier
- Title
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Controlling chaotic itinerancy in laser dynamics for reinforcement learningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-12Full publication date if available
- Authors
-
Ryugo Iwami, Takatomo Mihana, Kazutaka Kanno, Satoshi Sunada, Makoto Naruse, Atsushi UchidaList of authors in order
- Landing page
-
https://arxiv.org/abs/2205.05987Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2205.05987Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2205.05987Direct OA link when available
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Chaotic, Reinforcement learning, Computer science, Attractor, Scalability, Photonics, Task (project management), Artificial intelligence, Physics, Engineering, Mathematics, Optics, Database, Systems engineering, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ultrafast | 81 |
| abstract_inverted_index.approaches | 114 |
| abstract_inverted_index.artificial | 1 |
| abstract_inverted_index.brain-like | 40 |
| abstract_inverted_index.completely | 100 |
| abstract_inverted_index.controlled | 89 |
| abstract_inverted_index.injection. | 92 |
| abstract_inverted_index.itinerancy | 53, 127 |
| abstract_inverted_index.multi-mode | 56 |
| abstract_inverted_index.properties | 15 |
| abstract_inverted_index.competition | 87 |
| abstract_inverted_index.controlling | 50 |
| abstract_inverted_index.effectively | 129 |
| abstract_inverted_index.exploration | 97 |
| abstract_inverted_index.fundamental | 73 |
| abstract_inverted_index.itinerancy, | 26 |
| abstract_inverted_index.large-scale | 116 |
| abstract_inverted_index.multi-armed | 68 |
| abstract_inverted_index.spontaneous | 29 |
| abstract_inverted_index.accelerating | 8 |
| abstract_inverted_index.considerable | 5 |
| abstract_inverted_index.conventional | 104, 113 |
| abstract_inverted_index.higher-order | 23 |
| abstract_inverted_index.intelligence | 2 |
| abstract_inverted_index.accelerators. | 138 |
| abstract_inverted_index.outperforming | 111 |
| abstract_inverted_index.reinforcement | 75 |
| abstract_inverted_index.semiconductor | 57 |
| abstract_inverted_index.functionalities. | 24, 41 |
| abstract_inverted_index.quasi-attractors, | 34 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |