Multi-agent reinforcement learning (MARL) studies crucial principles that are applicable to a variety of fields, including wireless networking and autonomous driving. We propose a photonic-based decision-making algorithm to address one of the most fundamental problems in MARL, called the competitive multi-armed bandit (CMAB) problem. Our numerical simulations demonstrate that chaotic oscillations and cluster synchronization of optically coupled lasers, along with our proposed decentralized coupling adjustment, efficiently balance exploration and exploitation while facilitating cooperative decision-making without explicitly sharing information among agents. Our study demonstrates how decentralized reinforcement learning can be achieved by exploiting complex physical processes controlled by simple algorithms.

Photonic accelerators [1] have been gaining attention in recent years, and a variety of implementations and applications have now been explored [2-9]. These advancements can be attributed to a growing awareness of the saturating speed of performance improvements in conventional computational systems [10], despite the soaring demands for information processing in an extensive range of applications, especially in machine learning. Reinforcement learning [11] is a subfield of machine learning that involves optimizing computer outputs or actions to maximize the reward function. Its applications are now essential to our daily lives, ranging from self-driving vehicles [12] and targeted advertising [13] to wireless networking [14], and there is now a strong demand for computational acceleration. Specifically, what we focus on here is decision-making.