Exploring Deep Reinforcement Learning with Multi Q-Learning

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Intelligent Control and Automation

ISSN Print: 2153-0653
ISSN Online: 2153-0661
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E-mail: ica@scirp.org

Google-based Impact Factor: 0.70
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"Exploring Deep Reinforcement Learning with Multi Q-Learning"
written by Ethan Duryea, Michael Ganger, Wei Hu,
published by Intelligent Control and Automation, Vol.7 No.4, 2016

has been cited by the following article(s):

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CrossRef

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[2]	Autonomous and cooperative control of UAV cluster with multi-agent reinforcement learning
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[3]	A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals
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[4]	Comprehensive and Self‐Contained Introduction to Deep Reinforcement Learning
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[5]	Distribution Grid planning by self-designing with reinforcement learning
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[6]	Reinforcement Learning with Deep Q-Networks
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[7]	面向分布式电网的多区域协同控制方法研究.
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[8]	Application of reinforcement learning algorithm in delivery order system under supply chain environment
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[9]	Computational Modeling of Multi-Agent, Continuous Decision Making in Competitive Contexts
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[10]	Wind Farm Power Generation Control via Double-Network-Based Deep Reinforcement Learning
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[11]	Several Reinforcement Learning Methods in Mean-Field Games with Binary Action Spaces
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[12]	基于多智能体深度确定策略梯度算法的有功-无功协调调度模型
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[13]	The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions
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[14]	A Q-values sharing framework for multi-agent reinforcement learning under budget constraint
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[15]	Deep Reinforcement Learning for Energy-efficient Train Operation of Automatic Driving
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[16]	IPro: An Approach for Intelligent SDN Monitoring
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[17]	Accelerating Reinforcement Learning with Prioritized Experience Replay for Maze Game
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[18]	Gradient boosting in crowd ensembles for Q-learning using weight sharing
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[19]	Run-to-Run Control of Chemical Mechanical Polishing Process Based on Deep Reinforcement Learning
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[20]	Deep Reinforcement Learning Based Optimal Schedule for a Battery Swapping Station Considering Uncertainties
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[21]	Weighted Densely Connected Convolutional Networks for Reinforcement Learning
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[22]	Quantum Multiple Q-Learning
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[23]	Distributional Reinforcement Learning with Quantum Neural Networks
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[24]	Reinforcement Learning with Deep Quantum Neural Networks
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[25]	Morphing Control of a New Bionic Morphing UAV with Deep Reinforcement Learning
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[26]	Approximate Policy-Based Accelerated Deep Reinforcement Learning
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[27]	利用空间优化的增强学习 Sarsa 改进预取算法
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[28]	Multi Pseudo Q-Learning-Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles
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[29]	Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The
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[30]	Empirical Analysis of Decision Making of an AI Agent on IBM's 5Q Quantum Computer
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[31]	Architecture of Management Game for Reinforced Deep Learning
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[32]	Bayesian Nonparametric Models Characterize Instantaneous Strategies in a Competitive Dynamic Game
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[33]	基于加权密集连接卷积的深度强化学习
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[34]	Aprendizado profundo: conceitos, técnicas e es-tudo de caso de análise de imagens com Java
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[35]	深度强化学习在智能制造中的应用展望综述
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[2]	Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning Journal of Hydrology, 2023 DOI:10.1016/j.jhydrol.2023.129973

[3]	A novel duplex deep reinforcement learning based RRM framework for next-generation V2X communication networks Expert Systems with Applications, 2023 DOI:10.1016/j.eswa.2023.121004

[4]	The Wisdom of the Crowd: Reliable Deep Reinforcement Learning Through Ensembles of Q-Functions IEEE Transactions on Neural Networks and Learning Systems, 2023 DOI:10.1109/TNNLS.2021.3089425

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[7]	The Wisdom of the Crowd: Reliable Deep Reinforcement Learning Through Ensembles of Q-Functions IEEE Transactions on Neural Networks and Learning Systems, 2023 DOI:10.1109/TNNLS.2021.3089425

[8]	Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning Journal of Hydrology, 2023 DOI:10.1016/j.jhydrol.2023.129973

[9]	Review of Deep Reinforcement Learning 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2022 DOI:10.1109/IMCEC55388.2022.10020015

[10]	Review of Deep Reinforcement Learning 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2022 DOI:10.1109/IMCEC55388.2022.10020015

[11]	The research on intelligent cooperative combat of UAV cluster with multi-agent reinforcement learning Aerospace Systems, 2022 DOI:10.1007/s42401-021-00105-x

[12]	Artificial Intelligent Techniques for Wireless Communication and Networking 2022 DOI:10.1002/9781119821809.ch1

[13]	Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning IEEE Transactions on Industrial Informatics, 2022 DOI:10.1109/TII.2021.3095563

[14]	Autonomous and cooperative control of UAV cluster with multi-agent reinforcement learning The Aeronautical Journal, 2022 DOI:10.1017/aer.2021.112

[15]	A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals Applied Sciences, 2022 DOI:10.3390/app12178880

[16]	A priority-aware scheduling framework for heterogeneous workloads in container-based cloud Applied Intelligence, 2022 DOI:10.1007/s10489-022-04164-1

[17]	Application of Reinforcement Learning Algorithm in Delivery Order System under Supply Chain Environment Mobile Information Systems, 2021 DOI:10.1155/2021/5880795

[18]	Deep Learning for Unmanned Systems Studies in Computational Intelligence, 2021 DOI:10.1007/978-3-030-77939-9_9

[19]	IPro: An approach for intelligent SDN monitoring Computer Networks, 2020 DOI:10.1016/j.comnet.2020.107108

[20]	Gradient boosting in crowd ensembles for Q-learning using weight sharing International Journal of Machine Learning and Cybernetics, 2020 DOI:10.1007/s13042-020-01115-5

[21]	Weighted Densely Connected Convolutional Networks for Reinforcement Learning International Journal of Pattern Recognition and Artificial Intelligence, 2020 DOI:10.1142/S0218001420520011

[22]	Approximate Policy-Based Accelerated Deep Reinforcement Learning IEEE Transactions on Neural Networks and Learning Systems, 2020 DOI:10.1109/TNNLS.2019.2927227

[23]	Run-to-Run Control of Chemical Mechanical Polishing Process Based on Deep Reinforcement Learning IEEE Transactions on Semiconductor Manufacturing, 2020 DOI:10.1109/TSM.2020.3002896

[24]	Deep Reinforcement Learning Based Optimal Schedule for a Battery Swapping Station Considering Uncertainties IEEE Transactions on Industry Applications, 2020 DOI:10.1109/TIA.2020.2986412

[25]	Deep Reinforcement Learning for Energy-efficient Train Operation of Automatic Driving 2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT), 2020 DOI:10.1109/ICCSNT50940.2020.9305007

[26]	A Q-values Sharing Framework for Multi-agent Reinforcement Learning under Budget Constraint ACM Transactions on Autonomous and Adaptive Systems, 2020 DOI:10.1145/3447268

[27]	A Q-values Sharing Framework for Multi-agent Reinforcement Learning under Budget Constraint ACM Transactions on Autonomous and Adaptive Systems, 2020 DOI:10.1145/3447268

[28]	Reinforcement Learning with Deep Quantum Neural Networks Journal of Quantum Information Science, 2019 DOI:10.4236/jqis.2019.91001

[29]	Intelligent Systems and Applications Advances in Intelligent Systems and Computing, 2019 DOI:10.1007/978-3-030-01054-6_4

[30]	Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game Nature Communications, 2019 DOI:10.1038/s41467-019-09789-4

[31]	Morphing control of a new bionic morphing UAV with deep reinforcement learning Aerospace Science and Technology, 2019 DOI:10.1016/j.ast.2019.05.058

[32]	Multi Pseudo Q-Learning-Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles IEEE Transactions on Neural Networks and Learning Systems, 2019 DOI:10.1109/TNNLS.2018.2884797

[33]	Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game Nature Communications, 2019 DOI:10.1038/s41467-019-09789-4

[34]	Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game Nature Communications, 2019 DOI:10.1038/s41467-019-09789-4

[35]	Empirical Analysis of Decision Making of an AI Agent on IBM’s 5Q Quantum Computer Natural Science, 2018 DOI:10.4236/ns.2018.101004

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

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