Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network
This work addresses decentralized decision-making in fields like wireless networking and autonomous driving, but it appears incremental as it applies a novel physical method to a known bottleneck.
The paper tackled the competitive multi-armed bandit problem in multi-agent reinforcement learning by proposing a photonic-based algorithm using chaotic laser networks, achieving efficient exploration-exploitation balance and cooperative decision-making without explicit information sharing.
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.