ArticleScientific reports2017
Ultrafast photonic reinforcement learning based on laser chaos.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Conflict-free joint decision by lag and zero-lag synchronization in laser network.Scientific reports · 2024Article
- Deep neural network-based phase calibration in integrated optical phased arrays.Scientific reports · 2023Article
- Harnessing microcomb-based parallel chaos for random number generation and optical decision making.Nature communications · 2023Article
- 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
- Decision making for large-scale multi-armed bandit problems using bias control of chaotic temporal waveforms in semiconductor lasers.Scientific reports · 2022Article
- Entangled and correlated photon mixed strategy for social decision making.Scientific reports · 2021Article
- Entangled N-photon states for fair and optimal social decision making.Scientific reports · 2020Article
- Chaotic Signatures Exhibited by Plasmonic Effects in Au Nanoparticles with Cells.Sensors (Basel, Switzerland) · 2019Article
- Generative adversarial network based on chaotic time series.Scientific reports · 2019Article
- Entangled-photon decision maker.Scientific reports · 2019Article
- A single shot coherent Ising machine based on a network of injection-locked multicore fiber lasers.Nature communications · 2019Article
- On-chip photonic decision maker using spontaneous mode switching in a ring laser.Scientific reports · 2019Article
- Scalable photonic reinforcement learning by time-division multiplexing of laser chaos.Scientific reports · 2018Article
- Why is the environment important for decision making? Local reservoir model for choice-based learning.PloS one · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reinforcement learning involves decision making in dynamic and uncertain environments and constitutes an important element of artificial intelligence (AI). In this work, we experimentally demonstrate that the ultrafast chaotic oscillatory dynamics of lasers efficiently solve the multi-armed bandit problem (MAB), which requires decision making concerning a class of difficult trade-offs called the exploration-exploitation dilemma. To solve the MAB, a certain degree of randomness is required for exploration purposes. However, pseudorandom numbers generated using conventional electronic circuitry encounter severe limitations in terms of their data rate and the quality of randomness due to their algorithmic foundations. We generate laser chaos signals using a semiconductor laser sampled at a maximum rate of 100 GSample/s, and combine it with a simple decision-making principle called tug of war with a variable threshold, to ensure ultrafast, adaptive, and accurate decision making at a maximum adaptation speed of 1 GHz. We found that decision-making performance was maximized with an optimal sampling interval, and we highlight the exact coincidence between the negative autocorrelation inherent in laser chaos and decision-making performance. This study paves the way for a new realm of ultrafast photonics in the age of AI, where the ultrahigh bandwidth of light wave can provide new value.
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Registered trials
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