Memory Effect on Adaptive Decision Making with a Chaotic Semiconductor Laser

Mihana, Takatomo; Terashima, Yuta; Naruse, Makoto; Kim, Song-Ju; Uchida, Atsushi

doi:10.1155/2018/4318127

Cited by 18 publications

(27 citation statements)

References 23 publications

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“…The threshold value related to the MSB of the slot machine is updated according to where α is referred to as the forgetting (memory) parameter 12 , 13 and Δ is the constant increment (in this experiment, Δ = 1 and α = 0.99). The intuitive meaning of the update given by Eq.…”

Section: Methodsmentioning

confidence: 99%

“…In the present study, the amount of threshold adjustment in the case of wining is given by 1 while that of losing is given by the parameter Ω. In view of the adaptation in dynamically changing environments, forgetting past events is also important; hence, we introduced forgetting (memory) parameters 12 , 13 for all threshold values. The detailed definition is provided in the Methods section.…”

Section: Principlementioning

confidence: 99%

“…In our previous study, we experimentally demonstrated that the ultrafast chaotic oscillatory dynamics of lasers 2 – 5 can be used to solve the MAB efficiently 12 , 13 . With a chaotic time series generated by a semiconductor laser with a delayed feedback sampled at a maximum rate of 100 GSample/s followed by a digitization mechanism with a variable threshold, ultrafast, adaptive, and accurate decision-making was demonstrated.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Naruse

Mihana

Hori

et al. 2018

Sci Rep

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“…If at the t -th play, the current (mA) is injected to controller BC 1 whereas is injected to BC 2 if . The amount of C ( t ) is updated in accordance with the results of slot machine playing as follows:andwhere is the memory parameter (typically, ≈0.99–0.999) 37 , and Δ is an incremental parameter ( in this study). in Eq.…”

Section: Principle Of Ring-laser-based Decision-makingmentioning

confidence: 99%

On-chip photonic decision maker using spontaneous mode switching in a ring laser

Homma

Kochi

Niiyama

et al. 2019

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Efficient and accurate decision making is gaining increased importance with the rapid expansion of information communication technologies including artificial intelligence. Here, we propose and experimentally demonstrate an on-chip, integrated photonic decision maker based on a ring laser. The ring laser exhibits spontaneous switching between clockwise and counter-clockwise oscillatory dynamics; we utilize such nature to solve a multi-armed bandit problem. The spontaneous switching dynamics provides efficient exploration to find the accurate decision. On-line decision making is experimentally demonstrated including autonomous adaptation to an uncertain environment. This study paves the way for directly utilizing the fluctuating physics inherent in ring lasers, or integrated photonics technologies in general, for achieving or accelerating intelligent functionality.

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“…Mihana et al . examined the impact of the degree of forgetting (or lifetime impact) in adaptive decision making solved by a chaotic laser system [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Why is the environment important for decision making? Local reservoir model for choice-based learning

et al. 2018

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Decision making based on behavioral and neural observations of living systems has been extensively studied in brain science, psychology, neuroeconomics, and other disciplines. Decision-making mechanisms have also been experimentally implemented in physical processes, such as single photons and chaotic lasers. The findings of these experiments suggest that there is a certain common basis in describing decision making, regardless of its physical realizations. In this study, we propose a local reservoir model to account for choice-based learning (CBL). CBL describes decision consistency as a phenomenon where making a certain decision increases the possibility of making that same decision again later. This phenomenon has been intensively investigated in neuroscience, psychology, and other related fields. Our proposed model is inspired by the viewpoint that a decision is affected by its local environment, which is referred to as a local reservoir. If the size of the local reservoir is large enough, consecutive decision making will not be affected by previous decisions, thus showing lower degrees of decision consistency in CBL. In contrast, if the size of the local reservoir decreases, a biased distribution occurs within it, which leads to higher degrees of decision consistency in CBL. In this study, an analytical approach for characterizing local reservoirs is presented, as well as several numerical demonstrations. Furthermore, a physical architecture for CBL based on single photons is discussed, and the effects of local reservoirs are numerically demonstrated. Decision consistency in human decision-making tasks and in recruiting empirical data is evaluated based on the local reservoir. This foundation based on a local reservoir offers further insights into the understanding and design of decision making.

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Memory Effect on Adaptive Decision Making with a Chaotic Semiconductor Laser

Cited by 18 publications

References 23 publications

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

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

On-chip photonic decision maker using spontaneous mode switching in a ring laser

Why is the environment important for decision making? Local reservoir model for choice-based learning

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