ArticleScientific reports2021
Arm order recognition in multi-armed bandit problem with laser chaos time series.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Efficient dynamic channel assignment through laser chaos: a multiuser parallel processing learning algorithm.Scientific reports · 2023Article
- Controlling chaotic itinerancy in laser dynamics for reinforcement learning.Science advances · 2022Article
- Order recognition by Schubert polynomials generated by optical near-field statistics via nanometre-scale photochromism.Scientific reports · 2022Article
- Entangled and correlated photon mixed strategy for social decision making.Scientific reports · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects the arm with the highest reward expectation, the correct recognition of the order of arms in terms of reward expectations is not achievable. Here, we present an algorithm where the degree of exploration is adaptively controlled based on confidence intervals that represent the estimation accuracy of reward expectations. We have demonstrated numerically that our approach did improve arm order recognition accuracy significantly, along with reduced dependence on reward environments, and the total reward is almost maintained compared with conventional MAB methods. This study applies to sectors where the order information is critical, such as efficient allocation of resources in information and communications technology.
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Registered trials
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