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Deep Q-Learning Based Optimal query routing Approach for Unstructured P2P Network

Computers, Materials & Continua 2022年第3期70卷 5765-5781页

作者： Mohammad Shoab Abdullah Shawan Alotaibi Department of Computer Science Faculty of Science at Al DawadmiShaqra UniversityShaqraSaudi Arabi

Deep Reinforcement Learning(DRL)is a class of Machine Learning(ML)that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future *** has been used in many application fields,including games,robots,networks,*** creating autonomous systems that improve themselves with *** is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing ***,a novel query routing approach called Deep Reinforcement Learning based Route Selection(DRLRS)is proposed for unstructured P2P networks based on a Deep Q-Learning *** main objective of this approach is to achieve better retrieval effectiveness with reduced searching cost by less number of connected peers,exchangedmessages,and reduced *** simulation results shows a significantly improve searching a resource with compression to k-Random Walker and Directed ***,retrieval effectiveness,search cost in terms of connected peers,and average overhead are 1.28,106,149,respectively.

关键词： Reinforcement learning deep q-learning unstructured p2p network query routing

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