<< Multiagent reinforcement learning (MARL) studies crucial principles that are applicable to a variety of fields, including wireless networking and autonomous driving. (AA) propose a photonic-based decision-making algorithm to address one of the most fundamental problems in MARL, called the competitive multiarmed bandit (CMAB) problem. >>
AA << demonstrate that chaotic oscillations and cluster synchronization of optically coupled lasers, along with (their) proposed decentralized coupling adjustment, efficiently balance exploration and exploitation while facilitating cooperative decision making without explicitly sharing information among agents. >>
AA << study demonstrates how decentralized reinforcement learning can be achieved by exploiting complex physical processes controlled by simple algorithms. >>
Shun Kotoku, Takatomo Mihana, et al. Decentralized multiagent reinforcement learning algorithm using a cluster-synchronized laser network. Phys. Rev. E 110, 064212. Dec 11, 2024.
Also: game, chaos, ai (artificial intell), in https://www.inkgmr.net/kwrds.html
Keywords: game, cooperation, chaos, exploration, exploitation, ai, artificial intelligence, MARL, CMAB.
