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Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network
Multi-agent reinforcement learning (MARL) studies crucial principles that are applicable to a variety of fields, including wireless networking and autonomous driving.
Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network
Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing.
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