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+studies+crucial+principles+that+are+applicable+to+a+variety+of+fields%252C+including+wireless+networking+and+autonomous+driving.+We+propose+a+photonic-based+decision-making+algorithm+to+address+one+of+the+most+fundamental+problems+in+MARL%252C+called+the+competitive+multi-armed+bandit+(CMAB)+problem.+Our+numerical+simulations+demonstrate+that+chaotic+oscillations+and+cluster+synchronization+of+optically+coupled+lasers%252C+along+with+our+proposed+decentralized+coupling+adjustment%252C+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.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.e08732945b5e72ca197f4071b06bc3b1%3frik%3dFCxx4xVLmd3V6Q%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Multi-Agent+Reinforcement+Learning+in+Cloud+Computing+Figure&FORM=IRPRST&ck=ADB66FDDDB856FF91C1FC19811213124&selectedIndex=23&itb=0)
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