  Reinforcement learning involves decision making in dynamic and uncertain
environments, and constitutes one important element of artificial intelligence
(AI). In this paper, 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,
pseudo-random 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
photons can provide new value.
