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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/82292


    Title: 實現創新V2X車眾智慧合作共享服務的自駕車雲網的關鍵技術研究-子計畫二:運用於車載網路資源分配之多代理人增強式學習方法;Multi-Agent Reinforcement Learning for Resource Allocation over Vehicular Networks
    Authors: 黃志煒
    Contributors: 國立中央大學通訊工程學系
    Keywords: 車聯網;資源分配;增強式學習;深度學習;V2X;resource allocation;reinforcement learning;deep learning
    Date: 2020-01-13
    Issue Date: 2020-01-13 14:37:24 (UTC+8)
    Publisher: 科技部
    Abstract: 在蓬勃發展的智慧物聯網 (Internet of Things, IoT) 趨勢下,各類車輛可被視為具移動能力的智慧代理人 (smart agent),透過共享通訊、儲存、及運算資源,共同完成網路中的應用。多代理人增強式學習 (multi-agent reinforcement learning, MARL),則被視為在充滿不確定性及本質上不穩定的車聯網環境下,找到良好解決方法的學習架構之一,可透過車輛間的協同合作,為網路節點間學習一套新的決策模式及提高多代理人系統 (multi-agent system, MAS) 的效能帶來共同的效益,以處理不斷動態變動的環境。在此架構下,smart agent 間對於資源運用的互動情境,可視為一由無線網路連結起來的 MAS,用其分散式決策的特性與邊緣運算 (edge computing) 整合,來因應車聯網中的高度互動性問題效果可期,且尚未有完整的研究成果,值得深入探討。我們規劃從發展新的 MAS 架構出發,再將此架構運用在最重要的 V2X 串流與關鍵任務應用上,延續執行中計劃對於 RL 運用在網路資源分配的經驗,深入研究 MARL 結合深度學習的應用效果,並開放原始碼以供驗證。本計劃之創新性在於透過深入了解多代理人深度增強式學習技術,提出領先且可行之車聯網資源管理方法,並做為進一步研究 V2X 服務之重要參考。 ;In the trend of the Internet of Things, vehicles can be considered as smart agents collaborated to provide services through shared communication, storage, and computing resources. The multi-agent reinforcement learning (MARL) technic can be a proper tool to analysis V2X resource allocation issues. The multi-agent system formed by vehicles can be utilized to achieve distributed decisions through edge computing architecture. However, the solution to this topic is still emerging and worth further study. In this proposal, we plan to begin with a new clustering-first MAS architecture, and then apply it to streaming and mission-critical V2X applications. The effectiveness of MARL will be investigated and target for innovative resource allocation for future V2X applications.
    Relation: 財團法人國家實驗研究院科技政策研究與資訊中心
    Appears in Collections:[通訊工程學系] 研究計畫

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