Random walk with chaotically driven bias

Kim, Song-Ju; Naruse, Makoto; Aono, Masashi; Hori, Hirokazu; Akimoto, Takuma

doi:10.1038/srep38634

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Meanwhile, besides negative autocorrelation inherent in laser chaos, other perspectives could address the underlying mechanism such as diffusivity, 44 Hurst exponents, 45 etc.…”

Section: Discussionmentioning

confidence: 99%

Ultrafast photonic reinforcement learning based on laser chaos

Naruse

Terashima

Uchida

et al. 2017

Sci Rep

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110

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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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“…Meanwhile, besides negative autocorrelation inherent in laser chaos, other perspectives could address the underlying mechanism such as diffusivity, 44 Hurst exponents, 45 etc.…”

Section: Discussionmentioning

confidence: 99%

Ultrafast photonic reinforcement learning based on laser chaos

Naruse

Terashima

Uchida

et al. 2017

Sci Rep

Self Cite

110

View full text Add to dashboard Cite

show abstract

“…In this respect, we analysed the diffusivity of the temporal sequences based on the ensemble averages of the time-averaged mean square displacements (ETMSDs) 28 , 29 in the following manner. We first generated a random walker via comparison between the chaotic time series.…”

Section: Discussionmentioning

confidence: 99%

Scalable photonic reinforcement learning by time-division multiplexing of laser chaos

Naruse

Mihana

Hori

et al. 2018

Sci Rep

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Reinforcement learning involves decision-making in dynamic and uncertain environments and constitutes a crucial element of artificial intelligence. In our previous work, we experimentally demonstrated that the ultrafast chaotic oscillatory dynamics of lasers can be used to efficiently solve the two-armed bandit problem, which requires decision-making concerning a class of difficult trade-offs called the exploration–exploitation dilemma. However, only two selections were employed in that research; hence, the scalability of the laser-chaos-based reinforcement learning should be clarified. In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time series. The experimental demonstrations in which bandit problems with up to 64 arms were successfully solved are presented where laser chaos time series significantly outperforms quasiperiodic signals, computer-generated pseudorandom numbers, and coloured noise. Detailed analyses are also provided that include performance comparisons among laser chaos signals generated in different physical conditions, which coincide with the diffusivity inherent in the time series. This study paves the way for ultrafast reinforcement learning by taking advantage of the ultrahigh bandwidths of light wave and practical enabling technologies.

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“…This is evident in the fact that people have used objects and natural phenomena to apply randomness in gambling since ancient times [42]. In terms of search methods, for example, some results demonstrate that methods that consider chaos, which is a deterministic phenomenon, give more efficient search results than the method with randomness [43,44]. Certain features of near randomness but not belonging to randomness allow for the creation of special functions, such as efficient search.…”

Section: Discussionmentioning

confidence: 99%

Human Randomness in the Rock-Paper-Scissors Game

2022

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In this study, we investigated the human capacity to generate randomness in decision-making processes using the rock-paper-scissors (RPS) game. The randomness of the time series was evaluated using the time-series data of RPS moves made by 500 subjects who played 50 consecutive RPS games. The indices used for evaluation were the Lempel–Ziv complexity and a determinism index obtained from a recurrence plot, and these indicators represent the complexity and determinism of the time series, respectively. The acquired human RPS time-series data were compared to a pseudorandom RPS sequence generated by the Mersenne Twister and the RPS time series generated by the RPS game’s strategy learned using the human RPS time series acquired via genetic programming. The results exhibited clear differences in randomness among the pseudorandom number series, the human-generated series, and the AI-generated series.

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Random walk with chaotically driven bias

Cited by 9 publications

References 26 publications

Ultrafast photonic reinforcement learning based on laser chaos

Ultrafast photonic reinforcement learning based on laser chaos

Scalable photonic reinforcement learning by time-division multiplexing of laser chaos

Human Randomness in the Rock-Paper-Scissors Game

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