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The Multi-Armed Bandit (MAB) problem, foundational to reinforcementlearning-based decision-making, addresses the challenge of maximizing rewardsamidst multiple uncertain choices. While algorithmic solutions are effective,their computational efficiency diminishes with increasing problem complexity.Photonic accelerators, leveraging temporal and spatial-temporal chaos, haveemerged as promising alternatives. However, despite these advancements, currentapproaches either compromise computation speed or amplify system complexity. Inthis paper, we introduce a chaotic microresonator frequency comb (chaos comb)to tackle the MAB problem, where each comb mode is assigned to a slot machine.Through a proof-of-concept experiment, we employ 44 comb modes to address anMAB with 44 slot machines, demonstrating performance competitive with bothconventional software algorithms and other photonic methods. Further, thescalability of decision making is explored with up to 512 slot machines usingexperimentally obtained temporal chaos in different time slots. Power-lawscalability is achieved with an exponent of 0.96, outperforming conventionalsoftware-based algorithms. Moreover, we find that a numerically calculatedchaos comb accurately reproduces experimental results, paving the way fordiscussions on strategies to increase the number of slot machines.

Solving multi-armed bandit problems using a chaotic microresonator comb