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Mastering the game of Go with deep neural networks and tree search

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Cited by:

  1. Thomas Bolander, 2019. "What do we loose when machines take the decisions?," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 23(4), pages 849-867, December.
  2. De Moor, Bram J. & Gijsbrechts, Joren & Boute, Robert N., 2022. "Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management," European Journal of Operational Research, Elsevier, vol. 301(2), pages 535-545.
  3. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
  4. Anthony Coache & Sebastian Jaimungal, 2021. "Reinforcement Learning with Dynamic Convex Risk Measures," Papers 2112.13414, arXiv.org, revised Nov 2022.
  5. Bo Hu & Jiaxi Li & Shuang Li & Jie Yang, 2019. "A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR," Energies, MDPI, vol. 12(19), pages 1-15, September.
  6. Li, Hao & Misra, Siddharth, 2021. "Reinforcement learning based automated history matching for improved hydrocarbon production forecast," Applied Energy, Elsevier, vol. 284(C).
  7. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
  8. Tian Zhu & Merry H. Ma, 2022. "Deriving the Optimal Strategy for the Two Dice Pig Game via Reinforcement Learning," Stats, MDPI, vol. 5(3), pages 1-14, August.
  9. Xiaoyue Li & John M. Mulvey, 2023. "Optimal Portfolio Execution in a Regime-switching Market with Non-linear Impact Costs: Combining Dynamic Program and Neural Network," Papers 2306.08809, arXiv.org.
  10. Carbone, Anna & Jensen, Meiko & Sato, Aki-Hiro, 2016. "Challenges in data science: a complex systems perspective," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 1-7.
  11. Feng, Cong & Zhang, Jie & Zhang, Wenqi & Hodge, Bri-Mathias, 2022. "Convolutional neural networks for intra-hour solar forecasting based on sky image sequences," Applied Energy, Elsevier, vol. 310(C).
  12. Anthony Coache & Sebastian Jaimungal & 'Alvaro Cartea, 2022. "Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning," Papers 2206.14666, arXiv.org, revised May 2023.
  13. Chanjuan Liu & Junming Yan & Yuanye Ma & Tianhao Zhao & Qiang Zhang & Xiaopeng Wei, 2020. "An Adversarial Search Method Based on an Iterative Optimal Strategy," Mathematics, MDPI, vol. 8(9), pages 1-16, September.
  14. Pedro Afonso Fernandes, 2024. "Forecasting with Neuro-Dynamic Programming," Papers 2404.03737, arXiv.org.
  15. Lee, Dongkyu & Song, Junho, 2023. "Risk-informed operation and maintenance of complex lifeline systems using parallelized multi-agent deep Q-network," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
  16. J. de Curtò & I. de Zarzà, 2024. "Analysis of Transportation Systems for Colonies on Mars," Sustainability, MDPI, vol. 16(7), pages 1-28, April.
  17. Haoran Wang & Xun Yu Zhou, 2020. "Continuous‐time mean–variance portfolio selection: A reinforcement learning framework," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1273-1308, October.
  18. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
  19. Yichen Zhang & Gan He & Lei Ma & Xiaofei Liu & J. J. Johannes Hjorth & Alexander Kozlov & Yutao He & Shenjian Zhang & Jeanette Hellgren Kotaleski & Yonghong Tian & Sten Grillner & Kai Du & Tiejun Huan, 2023. "A GPU-based computational framework that bridges neuron simulation and artificial intelligence," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
  20. Hao, Peng & Wei, Zhensong & Bai, Zhengwei & Barth, Matthew J., 2020. "Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic and Signal Conditions," Institute of Transportation Studies, Working Paper Series qt2fv5063b, Institute of Transportation Studies, UC Davis.
  21. Jun Li & Wei Zhu & Jun Wang & Wenfei Li & Sheng Gong & Jian Zhang & Wei Wang, 2018. "RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-18, November.
  22. Morato, P.G. & Andriotis, C.P. & Papakonstantinou, K.G. & Rigo, P., 2023. "Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  23. Guangyuan Li & Baobao Song & Harinder Singh & V. B. Surya Prasath & H. Leighton Grimes & Nathan Salomonis, 2023. "Decision level integration of unimodal and multimodal single cell data with scTriangulate," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  24. Tambet Matiisen & Aqeel Labash & Daniel Majoral & Jaan Aru & Raul Vicente, 2022. "Do Deep Reinforcement Learning Agents Model Intentions?," Stats, MDPI, vol. 6(1), pages 1-17, December.
  25. Zhiwei (Tony) Qin & Xiaocheng Tang & Yan Jiao & Fan Zhang & Zhe Xu & Hongtu Zhu & Jieping Ye, 2020. "Introduction: Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning," Interfaces, INFORMS, vol. 50(5), pages 272-286, September.
  26. Nathan Companez & Aldeida Aleti, 2016. "Can Monte-Carlo Tree Search learn to sacrifice?," Journal of Heuristics, Springer, vol. 22(6), pages 783-813, December.
  27. Yuchen Zhang & Wei Yang, 2022. "Breakthrough invention and problem complexity: Evidence from a quasi‐experiment," Strategic Management Journal, Wiley Blackwell, vol. 43(12), pages 2510-2544, December.
  28. Eom, Yong Hwan & Chung, Yoong & Park, Minsu & Hong, Sung Bin & Kim, Min Soo, 2021. "Deep learning-based prediction method on performance change of air source heat pump system under frosting conditions," Energy, Elsevier, vol. 228(C).
  29. Xiao-Yang Liu & Hongyang Yang & Jiechao Gao & Christina Dan Wang, 2021. "FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance," Papers 2111.09395, arXiv.org.
  30. Moreira, Túlio Marcondes & de Faria, Jackson Geraldo & Vaz-de-Melo, Pedro O.S. & Medeiros-Ribeiro, Gilberto, 2023. "Development and validation of an AI-Driven model for the La Rance tidal barrage: A generalisable case study," Applied Energy, Elsevier, vol. 332(C).
  31. Kwok Tai Chui & Wadee Alhalabi & Sally Shuk Han Pang & Patricia Ordóñez de Pablos & Ryan Wen Liu & Mingbo Zhao, 2017. "Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications," Sustainability, MDPI, vol. 9(12), pages 1-23, December.
  32. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
  33. Lu Wang & Wenqing Ai & Tianhu Deng & Zuo‐Jun M. Shen & Changjing Hong, 2020. "Optimal production ramp‐up in the smartphone manufacturing industry," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 685-704, December.
  34. Keller, Alexander & Dahm, Ken, 2019. "Integral equations and machine learning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 2-12.
  35. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
  36. Yin, Linfei & Yu, Tao & Zhang, Xiaoshun & Yang, Bo, 2018. "Relaxed deep learning for real-time economic generation dispatch and control with unified time scale," Energy, Elsevier, vol. 149(C), pages 11-23.
  37. Darshit Mehta & Mustafizur Rahman & Kenji Aono & Shantanu Chakrabartty, 2022. "An adaptive synaptic array using Fowler–Nordheim dynamic analog memory," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  38. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
  39. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
  40. Yi Cheng & Chuzhi Zhao & Pradeep Neupane & Bradley Benjamin & Jiawei Wang & Tongsheng Zhang, 2023. "Applicability and Trend of the Artificial Intelligence (AI) on Bioenergy Research between 1991–2021: A Bibliometric Analysis," Energies, MDPI, vol. 16(3), pages 1-15, January.
  41. Dimitri P. Bertsekas, 2018. "Proximal algorithms and temporal difference methods for solving fixed point problems," Computational Optimization and Applications, Springer, vol. 70(3), pages 709-736, July.
  42. Christopher R. Madan, 2020. "Considerations for Comparing Video Game AI Agents with Humans," Challenges, MDPI, vol. 11(2), pages 1-12, August.
  43. Vijayakumar Varadarajan & Dweepna Garg & Ketan Kotecha, 2021. "An Efficient Deep Convolutional Neural Network Approach for Object Detection and Recognition Using a Multi-Scale Anchor Box in Real-Time," Future Internet, MDPI, vol. 13(12), pages 1-19, November.
  44. Martin Eling & Davide Nuessle & Julian Staubli, 2022. "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 205-241, April.
  45. Hemant Jain & Balaji Padmanabhan & Paul A. Pavlou & T. S. Raghu, 2021. "Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society," Information Systems Research, INFORMS, vol. 32(3), pages 675-687, September.
  46. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
  47. Bruno Gav{s}perov & Zvonko Kostanjv{c}ar, 2022. "Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model," Papers 2207.09951, arXiv.org.
  48. Amanda A. Volk & Robert W. Epps & Daniel T. Yonemoto & Benjamin S. Masters & Felix N. Castellano & Kristofer G. Reyes & Milad Abolhasani, 2023. "AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  49. Anthony Zador & Sean Escola & Blake Richards & Bence Ölveczky & Yoshua Bengio & Kwabena Boahen & Matthew Botvinick & Dmitri Chklovskii & Anne Churchland & Claudia Clopath & James DiCarlo & Surya Gangu, 2023. "Catalyzing next-generation Artificial Intelligence through NeuroAI," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
  50. Jonas Wanner & Lukas-Valentin Herm & Kai Heinrich & Christian Janiesch, 2022. "The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2079-2102, December.
  51. Alessio Brini & Daniele Tantari, 2021. "Deep Reinforcement Trading with Predictable Returns," Papers 2104.14683, arXiv.org, revised May 2023.
  52. Zhewei Zhang & Youngjin Yoo & Kalle Lyytinen & Aron Lindberg, 2021. "The Unknowability of Autonomous Tools and the Liminal Experience of Their Use," Information Systems Research, INFORMS, vol. 32(4), pages 1192-1213, December.
  53. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
  54. Robertas Damasevicius, 2023. "Progress, Evolving Paradigms and Recent Trends in Economic Analysis," Financial Economics Letters, Anser Press, vol. 2(2), pages 35-47, October.
  55. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
  56. JinHyo Joseph Yun & EuiSeob Jeong & Xiaofei Zhao & Sung Deuk Hahm & KyungHun Kim, 2019. "Collective Intelligence: An Emerging World in Open Innovation," Sustainability, MDPI, vol. 11(16), pages 1-15, August.
  57. Anton Kocheturov & Panos M. Pardalos & Athanasia Karakitsiou, 2019. "Massive datasets and machine learning for computational biomedicine: trends and challenges," Annals of Operations Research, Springer, vol. 276(1), pages 5-34, May.
  58. Shi, Chengchun & Luo, Shikai & Le, Yuan & Zhu, Hongtu & Song, Rui, 2022. "Statistically efficient advantage learning for offline reinforcement learning in infinite horizons," LSE Research Online Documents on Economics 115598, London School of Economics and Political Science, LSE Library.
  59. Brini, Alessio & Tantari, Daniele, 2023. "Deep reinforcement trading with predictable returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
  60. Daniel John & Martin Kaltschmitt, 2022. "Control of a PVT-Heat-Pump-System Based on Reinforcement Learning–Operating Cost Reduction through Flow Rate Variation," Energies, MDPI, vol. 15(7), pages 1-19, April.
  61. Thomas P. Novak & Donna L. Hoffman, 2019. "Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects," Journal of the Academy of Marketing Science, Springer, vol. 47(2), pages 216-237, March.
  62. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
  63. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
  64. Ricardo S. Alonso & Inés Sittón-Candanedo & Roberto Casado-Vara & Javier Prieto & Juan M. Corchado, 2020. "Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture," Sustainability, MDPI, vol. 12(14), pages 1-23, July.
  65. Yuchen Fang & Kan Ren & Weiqing Liu & Dong Zhou & Weinan Zhang & Jiang Bian & Yong Yu & Tie-Yan Liu, 2021. "Universal Trading for Order Execution with Oracle Policy Distillation," Papers 2103.10860, arXiv.org.
  66. Stefano Bromuri, 2019. "Dynamic heuristic acceleration of linearly approximated SARSA( $$\lambda $$ λ ): using ant colony optimization to learn heuristics dynamically," Journal of Heuristics, Springer, vol. 25(6), pages 901-932, December.
  67. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
  68. Zhang, Hao & Fan, Qinhao & Liu, Shang & Li, Shengbo Eben & Huang, Jin & Wang, Zhi, 2021. "Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine," Applied Energy, Elsevier, vol. 304(C).
  69. Jaeyong Kang & Jeonghwan Gwak, 2020. "Ensemble Learning of Lightweight Deep Learning Models Using Knowledge Distillation for Image Classification," Mathematics, MDPI, vol. 8(10), pages 1-18, September.
  70. Bodo Herzog & Sufyan Osamah, 2019. "Reverse Engineering of Option Pricing: An AI Application," IJFS, MDPI, vol. 7(4), pages 1-12, November.
  71. Ugur Karaboga & Pelin Vardarlier, 2020. "Examining the use of artificial intelligence in recruitment processes," Bussecon Review of Social Sciences (2687-2285), Bussecon International Academy, vol. 2(4), pages 1-17, December.
  72. Xiao-Yang Liu & Hongyang Yang & Qian Chen & Runjia Zhang & Liuqing Yang & Bowen Xiao & Christina Dan Wang, 2020. "FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance," Papers 2011.09607, arXiv.org, revised Mar 2022.
  73. Jin-Hong Lim & Jae-Hwan Kim & Jun-Ho Huh, 2023. "Recent trends and proposed response strategies of international standards related to shipbuilding equipment big data integration platform," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 863-884, February.
  74. Charles Mackin & Malte J. Rasch & An Chen & Jonathan Timcheck & Robert L. Bruce & Ning Li & Pritish Narayanan & Stefano Ambrogio & Manuel Gallo & S. R. Nandakumar & Andrea Fasoli & Jose Luquin & Alexa, 2022. "Optimised weight programming for analogue memory-based deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  75. Niccolo Pescetelli, 2021. "A Brief Taxonomy of Hybrid Intelligence," Forecasting, MDPI, vol. 3(3), pages 1-11, September.
  76. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
  77. Máté Kolat & Bálint Kővári & Tamás Bécsi & Szilárd Aradi, 2023. "Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
  78. Syed Ghazi Sarwat & Timoleon Moraitis & C. David Wright & Harish Bhaskaran, 2022. "Chalcogenide optomemristors for multi-factor neuromorphic computation," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
  79. Li Xia, 2020. "Risk‐Sensitive Markov Decision Processes with Combined Metrics of Mean and Variance," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2808-2827, December.
  80. Georgios D. Kontes & Georgios I. Giannakis & Víctor Sánchez & Pablo De Agustin-Camacho & Ander Romero-Amorrortu & Natalia Panagiotidou & Dimitrios V. Rovas & Simone Steiger & Christopher Mutschler & G, 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings," Energies, MDPI, vol. 11(12), pages 1-23, December.
  81. Agrim Gupta & Silvio Savarese & Surya Ganguli & Li Fei-Fei, 2021. "Embodied intelligence via learning and evolution," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
  82. Shi, Tao & Xu, Chang & Dong, Wenhao & Zhou, Hangyu & Bokhari, Awais & Klemeš, Jiří Jaromír & Han, Ning, 2023. "Research on energy management of hydrogen electric coupling system based on deep reinforcement learning," Energy, Elsevier, vol. 282(C).
  83. Antonio Hernández-Blanco & Boris Herrera-Flores & David Tomás & Borja Navarro-Colorado, 2019. "A Systematic Review of Deep Learning Approaches to Educational Data Mining," Complexity, Hindawi, vol. 2019, pages 1-22, May.
  84. Tingzhao Fu & Yubin Zang & Yuyao Huang & Zhenmin Du & Honghao Huang & Chengyang Hu & Minghua Chen & Sigang Yang & Hongwei Chen, 2023. "Photonic machine learning with on-chip diffractive optics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  85. Sabrina Evans & Paolo Turrini, 2023. "Improving Strategic Decisions in Sequential Games by Exploiting Positional Similarity," Games, MDPI, vol. 14(3), pages 1-13, April.
  86. Oleg Szehr, 2021. "Hedging of Financial Derivative Contracts via Monte Carlo Tree Search," Papers 2102.06274, arXiv.org, revised Apr 2021.
  87. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
  88. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
  89. Fernando Martinez-Plumed & Emilia Gomez Gutierrez & Jose Hernandez-Orallo, 2020. "AI Watch Assessing Technology Readiness Levels for Artificial Intelligence," JRC Research Reports JRC122014, Joint Research Centre.
  90. Peter Bossaerts & Shijie Huang & Nitin Yadav, 2020. "Exploiting Distributional Temporal Difference Learning to Deal with Tail Risk," Risks, MDPI, vol. 8(4), pages 1-20, October.
  91. Wei-Chang Yeh & Yu-Hsin Hsieh & Chia-Ling Huang, 2022. "Newly Developed Flexible Grid Trading Model Combined ANN and SSO algorithm," Papers 2211.12839, arXiv.org.
  92. Junfeng Zhang & Qing Xue, 2022. "Actor–critic-based decision-making method for the artificial intelligence commander in tactical wargames," The Journal of Defense Modeling and Simulation, , vol. 19(3), pages 467-480, July.
  93. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
  94. Rhodes, Andrew & Johnson, Justin & Wildenbeest, Matthijs, 2020. "Platform Design When Sellers Use Pricing Algorithms," CEPR Discussion Papers 15504, C.E.P.R. Discussion Papers.
  95. Asmat Ara Shaikh & K. Santhana Lakshmi & Korakod Tongkachok & Joel Alanya-Beltran & Edwin Ramirez-Asis & Julian Perez-Falcon, 2022. "Empirical analysis in analysing the major factors of machine learning in enhancing the e-business through structural equation modelling (SEM) approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 681-689, March.
  96. Xiao-Yang Liu & Ziyi Xia & Jingyang Rui & Jiechao Gao & Hongyang Yang & Ming Zhu & Christina Dan Wang & Zhaoran Wang & Jian Guo, 2022. "FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning," Papers 2211.03107, arXiv.org.
  97. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
  98. Liu, Zeyu & Li, Xueping & Khojandi, Anahita, 2022. "The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
  99. Tzong-Shiann Ho & Ting-Chia Weng & Jung-Der Wang & Hsieh-Cheng Han & Hao-Chien Cheng & Chun-Chieh Yang & Chih-Hen Yu & Yen-Jung Liu & Chien Hsiang Hu & Chun-Yu Huang & Ming-Hong Chen & Chwan-Chuen Kin, 2020. "Comparing machine learning with case-control models to identify confirmed dengue cases," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(11), pages 1-21, November.
  100. Shi, Chengchun & Zhang, Shengxing & Lu, Wenbin & Song, Rui, 2022. "Statistical inference of the value function for reinforcement learning in infinite-horizon settings," LSE Research Online Documents on Economics 110882, London School of Economics and Political Science, LSE Library.
  101. Jialiang Lin & Yao Yu & Jiaxin Song & Xiaodong Shi, 2022. "Detecting and analyzing missing citations to published scientific entities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2395-2412, May.
  102. Guan, Xiaoshu & Sun, Huabin & Hou, Rongrong & Xu, Yang & Bao, Yuequan & Li, Hui, 2023. "A deep reinforcement learning method for structural dominant failure modes searching based on self-play strategy," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
  103. Kenshi Abe & Yusuke Kaneko, 2020. "Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games," Papers 2007.02141, arXiv.org, revised Dec 2020.
  104. Minkyu Shin & Jin Kim & Minkyung Kim, 2020. "Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo," Papers 2012.15035, arXiv.org, revised Jan 2021.
  105. Sukriti Manna & Troy D. Loeffler & Rohit Batra & Suvo Banik & Henry Chan & Bilvin Varughese & Kiran Sasikumar & Michael Sternberg & Tom Peterka & Mathew J. Cherukara & Stephen K. Gray & Bobby G. Sumpt, 2022. "Learning in continuous action space for developing high dimensional potential energy models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  106. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
  107. Zechu Li & Xiao-Yang Liu & Jiahao Zheng & Zhaoran Wang & Anwar Walid & Jian Guo, 2021. "FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance," Papers 2111.05188, arXiv.org.
  108. Mehmet S. Ismail, 2022. "Optimin achieves super-Nash performance," Papers 2210.00625, arXiv.org.
  109. Alberto Signoroni & Alessandro Ferrari & Stefano Lombardi & Mattia Savardi & Stefania Fontana & Karissa Culbreath, 2023. "Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  110. Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klöckl, 2024. "Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 529-576, February.
  111. Shuai, Bin & Zhou, Quan & Li, Ji & He, Yinglong & Li, Ziyang & Williams, Huw & Xu, Hongming & Shuai, Shijin, 2020. "Heuristic action execution for energy efficient charge-sustaining control of connected hybrid vehicles with model-free double Q-learning," Applied Energy, Elsevier, vol. 267(C).
  112. Ashish Kumar & Roussos Dimitrakopoulos & Marco Maulen, 2020. "Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1795-1811, October.
  113. Yuchao Dong, 2022. "Randomized Optimal Stopping Problem in Continuous time and Reinforcement Learning Algorithm," Papers 2208.02409, arXiv.org, revised Sep 2023.
  114. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
  115. Haoran Wang & Xun Yu Zhou, 2019. "Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework," Papers 1904.11392, arXiv.org, revised May 2019.
  116. Valmir C. Barbosa, 2017. "Information Integration from Distributed Threshold-Based Interactions," Complexity, Hindawi, vol. 2017, pages 1-14, January.
  117. Shijun Wang & Baocheng Zhu & Chen Li & Mingzhe Wu & James Zhang & Wei Chu & Yuan Qi, 2020. "Riemannian Proximal Policy Optimization," Computer and Information Science, Canadian Center of Science and Education, vol. 13(3), pages 1-93, August.
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