基于集群同步激光网络的去中心化多智能体强化学习算法
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network
AI总结：
本研究提出基于光子的决策算法，结合光耦合激光器的混沌振荡、集群同步与去中心化耦合调整，解决多智能体强化学习中的竞争性多臂老虎机问题，无需智能体显式共享信息即可高效平衡探索与利用并实现协作决策，为去中心化强化学习提供了物理实现新路径。
摘要：
多智能体强化学习（MARL）研究适用于无线网络、自动驾驶等诸多领域的核心原理。我们提出一种基于光子的决策算法，以解决MARL中最基础的问题之一——竞争性多臂老虎机（CMAB）问题。数值模拟表明，光耦合激光器的混沌振荡与集群同步，结合我们提出的去中心化耦合调整机制，能高效平衡探索与利用，同时无需智能体间显式共享信息即可促成协作决策。本研究展示了如何利用由简单算法控制的复杂物理过程实现去中心化强化学习。
英文摘要：
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.
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