Bayesian Q learning method with Dyna architecture and prioritized sweeping by Jun YU, Quan LIU, Qi-ming FU, Hong-kun SUN, Gui-xing CHEN

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Cross-domain service chain mapping mechanism based on Q-learning by Hongqi ZHANG, Rui HUANG, Yingjie YANG, Dexian CHANG, Liancheng ZHANG

“…A partitioning algorithm was designed to solve the problem based on Q-learning mechanism under this framework. Simulation results show that the performances of this method are better than other traditional methods on average partition time, average mapping cost, and acceptance ratioof service chain mapping request.…”
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Q-learning global path planning for UAV navigation with pondered priorities by Kevin B. de Carvalho, Hiago de O.B. Batista, Leonardo A. Fagundes-Junior, Iure Rosa L. de Oliveira, Alexandre S. Brandão

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Transmission scheduling scheme based on deep Q learning in wireless network by Jiang ZHU, Tingting WANG, Yonghui SONG, Yali LIU

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Research on Q-learning based rate control approach for HTTP adaptive streaming by Li-rong XIONG, Jing-zhi LEI, Xin JIN

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Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning by Wei Hao, Donglei Rong, Kefu Yi, Qiang Zeng, Zhibo Gao, Wenguang Wu, Chongfeng Wei, Biljana Scepanovic

“…With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. …”
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Deployment method for vEPC virtualized network function via Q-learning by Quan YUAN, Hong-bo TANG, Kai-zhi HUANG, Xiao-lei WANG, Yu ZHAO

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Research on resource allocation algorithm of centralized and distributed Q-learning in machine communication by Yunhe YU, Jun SUN

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Q-learning based handoff algorithm for satellite system with ancillary terrestrial component by Dan-ni XIONG, Yi LI

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RISKS REDUCING THROUGH INTELLIGENT HEADLIGHT MANAGEMENT: OPTIMIZING Q-LEARNING FOR ELECTRIC VEHICLES by Pitchaya Jamjuntr, Chanchai Techawatcharapaikul, Pannee Suanpang

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Dynamic channel selection in unknown environment based on graphical game and multi-Q learning by Fang-wei LI, Yong-chuan TANG, Jiang ZHU

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Defense decision-making method based on incomplete information stochastic game and Q-learning by Hongqi ZHANG, Junnan YANG, Chuanfu ZHANG

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Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management by Sreyas Ramesh, Sukanth B N, Sri Jaswanth Sathyavarapu, Vishwash Sharma, Nippun Kumaar A. A., Manju Khanna

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A distributed CRN resource allocation algorithm based on CBR and cooperative Q-learning by Lin XU, Zhijin ZHAO

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A Deep Q-Learning Algorithm With Guaranteed Convergence for Distributed and Uncoordinated Operation of Cognitive Radios by Ankita Tondwalkar, Andres Kwasinski

Subjects: “…uncoordinated multi-agent deep Q-learning…”
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Q-learning Based Meta-Heuristics for Scheduling Bi-Objective Surgery Problems with Setup Time by Ruixue Zhang, Hui Yu, Adam Slowik, Kaizhou Gao

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Energy-efficient resource allocation method in mobile edge network based on double deep Q-learning by Peng YU, Junye ZHANG, Wenjing LI, Fanqin ZHOU, Lei FENG, Shu FU, Xuesong QIU

“…To improve the system energy efficiency in mobile edge networks, a resource allocation method based on double deep Q-learning(DDQL) for integration of communication, computing, storage resources was proposed for the downlink communication process under the network architecture of multiple tasks, end devices, edge gateways and edge servers.A resource allocation model was constructed, which took the minimization of average energy consumption of tasks as the optimization goal and set the constraints of task delay limits and communication, computing, and storage resource limits.According to the model characteristics, a suitable resource allocation model and method based on DDQL framework was proposed to make intelligent allocation decisions for communication and computing resources and allocate storage resources on demand.Simulation results show that the proposed DDQL-based solution can effectively solve the multi-task resource allocation problem with good converge and low time complexity, and it reduces the average energy consumption of tasks by at least 5% compared with the solving methods based on random algorithm, greedy algorithm, particle swarm optimization algorithm and deep Q-learning while ensuring the quality of service.…”
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Secure relay node selection method based on Q-learning for fog computing in 5G network by Shanshan TU, Jinliang YU, Yuan MENG, M WWAQAS, Lei LIU

Subjects: “…Q-learning…”
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Autonomous security analysis and penetration testing model based on attack graph and deep Q-learning network by Cheng FAN, Guoqing HU, Taojie DING, Zhanhua ZHANG

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Q-Learning-Driven Butterfly Optimization Algorithm for Green Vehicle Routing Problem Considering Customer Preference by Weiping Meng, Yang He, Yongquan Zhou

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