Parallel bandit architecture based on laser chaos for reinforcement learning Article Swipe
Takashi Urushibara
,
Nicolas Chauvet
,
Satoshi Kochi
,
Satoshi Sunada
,
Kazutaka Kanno
,
Atsushi Uchida
,
Ryoichi Horisaki
,
Makoto Naruse
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.09543
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2205.09543
Accelerating artificial intelligence by photonics is an active field of study aiming to exploit the unique properties of photons. Reinforcement learning is an important branch of machine learning, and photonic decision-making principles have been demonstrated with respect to the multi-armed bandit problems. However, reinforcement learning could involve a massive number of states, unlike previously demonstrated bandit problems where the number of states is only one. Q-learning is a well-known approach in reinforcement learning that can deal with many states. The architecture of Q-learning, however, does not fit well photonic implementations due to its separation of update rule and the action selection. In this study, we organize a new architecture for multi-state reinforcement learning as a parallel array of bandit problems in order to benefit from photonic decision-makers, which we call parallel bandit architecture for reinforcement learning or PBRL in short. Taking a cart-pole balancing problem as an instance, we demonstrate that PBRL adapts to the environment in fewer time steps than Q-learning. Furthermore, PBRL yields faster adaptation when operated with a chaotic laser time series than the case with uniformly distributed pseudorandom numbers where the autocorrelation inherent in the laser chaos provides a positive effect. We also find that the variety of states that the system undergoes during the learning phase exhibits completely different properties between PBRL and Q-learning. The insights obtained through the present study are also beneficial for existing computing platforms, not just photonic realizations, in accelerating performances by the PBRL algorithms and correlated random sequences.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2205.09543
- https://arxiv.org/pdf/2205.09543
- OA Status
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- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4307900468Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2205.09543Digital Object Identifier
- Title
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Parallel bandit architecture based on laser chaos for reinforcement learningWork title
- Type
-
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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2022-05-19Full publication date if available
- Authors
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Takashi Urushibara, Nicolas Chauvet, Satoshi Kochi, Satoshi Sunada, Kazutaka Kanno, Atsushi Uchida, Ryoichi Horisaki, Makoto NaruseList of authors in order
- Landing page
-
https://arxiv.org/abs/2205.09543Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2205.09543Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2205.09543Direct OA link when available
- Concepts
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Reinforcement learning, Computer science, Photonics, Multi-armed bandit, Temporal difference learning, Artificial intelligence, Chaotic, Exploit, Machine learning, Physics, Computer security, Regret, OpticsTop concepts (fields/topics) attached by OpenAlex
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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.inherent | 186 |
| abstract_inverted_index.insights | 220 |
| abstract_inverted_index.learning | 20, 44, 72, 112, 135, 209 |
| abstract_inverted_index.obtained | 221 |
| abstract_inverted_index.operated | 168 |
| abstract_inverted_index.organize | 105 |
| abstract_inverted_index.parallel | 115, 130 |
| abstract_inverted_index.photonic | 29, 88, 125, 235 |
| abstract_inverted_index.photons. | 18 |
| abstract_inverted_index.positive | 193 |
| abstract_inverted_index.problems | 56, 119 |
| abstract_inverted_index.provides | 191 |
| abstract_inverted_index.balancing | 143 |
| abstract_inverted_index.cart-pole | 142 |
| abstract_inverted_index.computing | 231 |
| abstract_inverted_index.different | 213 |
| abstract_inverted_index.important | 23 |
| abstract_inverted_index.instance, | 147 |
| abstract_inverted_index.learning, | 27 |
| abstract_inverted_index.photonics | 4 |
| abstract_inverted_index.problems. | 41 |
| abstract_inverted_index.undergoes | 206 |
| abstract_inverted_index.uniformly | 179 |
| abstract_inverted_index.Q-learning | 65 |
| abstract_inverted_index.adaptation | 166 |
| abstract_inverted_index.algorithms | 243 |
| abstract_inverted_index.artificial | 1 |
| abstract_inverted_index.beneficial | 228 |
| abstract_inverted_index.completely | 212 |
| abstract_inverted_index.correlated | 245 |
| abstract_inverted_index.platforms, | 232 |
| abstract_inverted_index.previously | 53 |
| abstract_inverted_index.principles | 31 |
| abstract_inverted_index.properties | 16, 214 |
| abstract_inverted_index.selection. | 100 |
| abstract_inverted_index.separation | 93 |
| abstract_inverted_index.sequences. | 247 |
| abstract_inverted_index.well-known | 68 |
| abstract_inverted_index.Q-learning, | 82 |
| abstract_inverted_index.Q-learning. | 161, 218 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.distributed | 180 |
| abstract_inverted_index.environment | 155 |
| abstract_inverted_index.multi-armed | 39 |
| abstract_inverted_index.multi-state | 110 |
| abstract_inverted_index.Accelerating | 0 |
| abstract_inverted_index.Furthermore, | 162 |
| abstract_inverted_index.accelerating | 238 |
| abstract_inverted_index.architecture | 80, 108, 132 |
| abstract_inverted_index.demonstrated | 34, 54 |
| abstract_inverted_index.intelligence | 2 |
| abstract_inverted_index.performances | 239 |
| abstract_inverted_index.pseudorandom | 181 |
| abstract_inverted_index.Reinforcement | 19 |
| abstract_inverted_index.realizations, | 236 |
| abstract_inverted_index.reinforcement | 43, 71, 111, 134 |
| abstract_inverted_index.autocorrelation | 185 |
| abstract_inverted_index.decision-making | 30 |
| abstract_inverted_index.implementations | 89 |
| abstract_inverted_index.decision-makers, | 126 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |