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Creators/Authors contains: "Liu, Siming"

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  1. ; ; ; ; (, IEEE)
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  2. ; ; ; ; ; (, IEEE)
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  3. ; ; ; ; (, IEEE Access)
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  4. ; ; ; ; ; (, IEEE)
    Information sharing among agents to jointly solve problems is challenging for multi-agent reinforcement learning algorithms (MARL) in smart environments. In this paper, we present a novel information sharing approach for MARL, which introduces a Team Information Matrix (TIM) that integrates scenario-independent spatial and environmental information combined with the agent's local observations, augmenting both individual agent's performance and global awareness during the MARL learning. To evaluate this approach, we conducted experiments on three multi-agent scenarios of varying difficulty levels implemented in Unity ML-Agents Toolkit. Experimental results show that the agents utilizing our TIM-Shared variation outperformed those using decentralized MARL and achieved comparable performance to agents employing centralized MARL. 
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  5. ; ; ; ; ; ; (, Journal of Lightwave Technology)
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  6. ; ; ; ; ; ; ; (, Computer Networks)
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  7. ; ; ; ; ; (, Journal of Lightwave Technology)
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  8. ; ; ; ; ; (, IEEE Photonics Technology Letters)
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  9. ; ; ; ; ; ; (, 2019 Optical Fiber Communications Conference and Exhibition (OFC))
    We experimentally demonstrate a converged hybrid bi-directional mobile fronthaul by integrating MMW and FSO links with real-time FPGA processing. We achieve long-term stability under practical 5G operation scenarios with EVM variations of <0.7% for 16-QAM. 
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  10. ; ; ; ; ; (, 2018 European Conference on Optical Communication (ECOC))
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