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A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications
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作者 Sepehr Soltani Ehsan Ghafourian +2 位作者 Reza Salehi Diego Martín Milad Vahidi 《Intelligent Automation & Soft Computing》 2024年第1期93-108,共16页
Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning method... Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain constraints.To this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless channels.To this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline learning.Then,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power.Finally,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems. 展开更多
关键词 deep reinforcement learning deep q learning multiple access channel power allocation
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Intelligent Voltage Control Method in Active Distribution Networks Based on Averaged Weighted Double Deep Q-network Algorithm
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作者 Yangyang Wang Meiqin Mao +1 位作者 Liuchen Chang Nikos D.Hatziargyriou 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2023年第1期132-143,共12页
High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(ADN)highly variable.These characteristics put great challenges to traditional voltage control... High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(ADN)highly variable.These characteristics put great challenges to traditional voltage control methods.Voltage control based on the deep Q-network(DQN)algorithm offers a potential solution to this problem because it possesses humanlevel control performance.However,the traditional DQN methods may produce overestimation of action reward values,resulting in degradation of obtained solutions.In this paper,an intelligent voltage control method based on averaged weighted double deep Q-network(AWDDQN)algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network(DDQN)algorithm.Using the proposed method,the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process(MDP)model which is solved by the AWDDQN algorithm.The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN.The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs,and compared with the DQN and DDQN algorithms based methods,and traditional mixed-integer nonlinear program based methods.The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones. 展开更多
关键词 Averaged weighted double deep q-network(AWDDqN) deep q learning active distribution network(ADN) voltage control electrical vehicle(EV)
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Cloud Resource Allocation Based on Deep Q-Learning Network
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作者 Zuocong Chen 《国际计算机前沿大会会议论文集》 2020年第1期666-675,共10页
The goal of resource allocation is to allocate the optimal resource to the candidate tasks,so that all the tasks can be finished in less time and the users’demands can be satisfied.To have better performance on the t... The goal of resource allocation is to allocate the optimal resource to the candidate tasks,so that all the tasks can be finished in less time and the users’demands can be satisfied.To have better performance on the time span,CPU usage ratio and the load balance compared with existed methods,it proposes an allocation method that can map the tasks to the resources effectively,where an optimal allocation program will be generated.Firstly,the resource allocation model for tasks was proposed and the goal function was designed.Afterward,the deep Q-learning algorithm was defined to get an optimal allocation program,and the algorithm was analyzed in detail.The experiment was implemented to verify the proposed method.The simulation experiments prove that the method in this paper can effectively implement task scheduling,which has the advantages of high CPU utilization,short scheduling time and strong load balancing ability. 展开更多
关键词 deep q learning Resource allocation Reinforcement learning Cloud computing
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基于强化学习的换道模型研究
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作者 黄幸文 郝海明 +1 位作者 张水潮 俞思宁 《电子技术与软件工程》 2021年第10期198-200,共3页
本文研究基于车联网环境设计了强化学习换道模型,首先,采用最优化建模的方法研究换道控制模型的约束条件、初始条件和目标函数。其次,面向Deep Q Learning的算法及需求,根据车辆的运动规律设计状态转移方法,将约束条件内嵌到仿真器中,... 本文研究基于车联网环境设计了强化学习换道模型,首先,采用最优化建模的方法研究换道控制模型的约束条件、初始条件和目标函数。其次,面向Deep Q Learning的算法及需求,根据车辆的运动规律设计状态转移方法,将约束条件内嵌到仿真器中,根据目标函数设计回报函数,并通过试验选择Deep Q Learning种的Dueling deep network structure (DDQN)模型设计求解算法。最后,通过数值试验验证三种模型的有效性,本研究显示强化学习换道模型具有作为自动驾驶车辆换道控制模块的潜力。 展开更多
关键词 换道模型 强化学习 deep q learning
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