ArticleScience advances2022
Controlling chaotic itinerancy in laser dynamics for reinforcement learning.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 36 citations in OpenAlex.
- Large-scale integrated optoelectronic chaos for machine learning acceleration.Nature communications · 2026Article
- Deep learning prediction of noise-driven nonlinear instabilities in fibre optics.Nature communications · 2025Article
- Motion Hologram: Jointly optimized hologram generation and motion planning for photorealistic 3D displays via reinforcement learning.Science advances · 2025Article
- Accelerated optimization in deep learning with a proportional-integral-derivative controller.Nature communications · 2024Article
- Conflict-free joint decision by lag and zero-lag synchronization in laser network.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photonic artificial intelligence has attracted considerable interest in accelerating machine learning; however, the unique optical properties have not been fully used for achieving higher-order functionalities. Chaotic itinerancy, with its spontaneous transient dynamics among multiple quasi-attractors, can be used to realize brain-like functionalities. In this study, we numerically and experimentally investigate a method for controlling the chaotic itinerancy in a multimode semiconductor laser to solve a machine learning task, namely, the multiarmed bandit problem, which is fundamental to reinforcement learning. The proposed method uses 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 use chaotic itinerancy for effectively solving complex machine learning tasks as photonic hardware accelerators.
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Registered trials
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