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Human-level control through deep reinforcement learning

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Cited by:

  1. Yuemin Zheng & Jin Tao & Qinglin Sun & Hao Sun & Zengqiang Chen & Mingwei Sun & Feng Duan, 2022. "Deep-Reinforcement-Learning-Based Active Disturbance Rejection Control for Lateral Path Following of Parafoil System," Sustainability, MDPI, vol. 15(1), pages 1-18, 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. Jacob W. Crandall & Mayada Oudah & Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael A. Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," TSE Working Papers 17-806, Toulouse School of Economics (TSE).
    • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," IAST Working Papers 17-68, Institute for Advanced Study in Toulouse (IAST).
    • Jacob Crandall & Mayada Oudah & Fatimah Ishowo-Oloko Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Post-Print hal-01897802, HAL.
  4. Qi Zhu & Ning Yuan & Donghai Guan, 2019. "Cognitive Driven Multilayer Self-Paced Learning with Misclassified Samples," Complexity, Hindawi, vol. 2019, pages 1-10, March.
  5. Ismael T Freire & Clement Moulin-Frier & Marti Sanchez-Fibla & Xerxes D Arsiwalla & Paul F M J Verschure, 2020. "Modeling the formation of social conventions from embodied real-time interactions," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-22, June.
  6. Jingwei Sun & Jian Li & Hang Zhang, 2019. "Human representation of multimodal distributions as clusters of samples," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-29, May.
  7. Noye, Sarah & Mulero Martinez, Rubén & Carnieletto, Laura & De Carli, Michele & Castelruiz Aguirre, Amaia, 2022. "A review of advanced ground source heat pump control: Artificial intelligence for autonomous and adaptive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
  8. 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.
  9. Li, Hao & Misra, Siddharth, 2021. "Reinforcement learning based automated history matching for improved hydrocarbon production forecast," Applied Energy, Elsevier, vol. 284(C).
  10. Tulika Saha & Sriparna Saha & Pushpak Bhattacharyya, 2020. "Towards sentiment aided dialogue policy learning for multi-intent conversations using hierarchical reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
  11. Lu, Jing & Meng, Yucan & Timmermans, Harry & Zhang, Anming, 2021. "Modeling hesitancy in airport choice: A comparison of discrete choice and machine learning methods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 230-250.
  12. 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).
  13. Omid Rafieian, 2023. "Optimizing User Engagement Through Adaptive Ad Sequencing," Marketing Science, INFORMS, vol. 42(5), pages 910-933, September.
  14. Randall Spain & Jonathan Rowe & Andy Smith & Benjamin Goldberg & Robert Pokorny & Bradford Mott & James Lester, 2022. "A reinforcement learning approach to adaptive remediation in online training," The Journal of Defense Modeling and Simulation, , vol. 19(2), pages 173-193, April.
  15. Mikhail Tokarev & Egor Palkin & Rustam Mullyadzhanov, 2020. "Deep Reinforcement Learning Control of Cylinder Flow Using Rotary Oscillations at Low Reynolds Number," Energies, MDPI, vol. 13(22), pages 1-11, November.
  16. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  17. O’Malley, Cormac & de Mars, Patrick & Badesa, Luis & Strbac, Goran, 2023. "Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation," Applied Energy, Elsevier, vol. 349(C).
  18. Parag Parashar & Chun Han Chen & Chandni Akbar & Sze Ming Fu & Tejender S Rawat & Sparsh Pratik & Rajat Butola & Shih Han Chen & Albert S Lin, 2019. "Analytics-statistics mixed training and its fitness to semisupervised manufacturing," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-18, August.
  19. Tao Chen & Qais Alsafasfeh & Hajir Pourbabak & Wencong Su, 2017. "The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey," Energies, MDPI, vol. 11(1), pages 1-17, December.
  20. Jinho Lee & Jaewoo Kang, 2020. "Effectively training neural networks for stock index prediction: Predicting the S&P 500 index without using its index data," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-20, April.
  21. 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).
  22. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
  23. Xu Chen & Xuan Di & Zechu Li, 2023. "Social Learning for Sequential Driving Dilemmas," Games, MDPI, vol. 14(3), pages 1-12, May.
  24. Antti J. Tanskanen, 2020. "Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms," Papers 2010.13471, arXiv.org, revised Feb 2022.
  25. Caputo, Cesare & Cardin, Michel-Alexandre & Ge, Pudong & Teng, Fei & Korre, Anna & Antonio del Rio Chanona, Ehecatl, 2023. "Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning," Applied Energy, Elsevier, vol. 335(C).
  26. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
  27. 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).
  28. J. de Curtò & I. de Zarzà, 2024. "Analysis of Transportation Systems for Colonies on Mars," Sustainability, MDPI, vol. 16(7), pages 1-28, April.
  29. Yukun Cheng & Xiaotie Deng & Dominik Scheder, 2022. "Recent studies of agent incentives in internet resource allocation and pricing," Annals of Operations Research, Springer, vol. 314(1), pages 49-76, July.
  30. Jiankai Gao & Yang Li & Bin Wang & Haibo Wu, 2023. "Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm," Energies, MDPI, vol. 16(7), pages 1-21, April.
  31. Imen Azzouz & Wiem Fekih Hassen, 2023. "Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach," Energies, MDPI, vol. 16(24), pages 1-18, December.
  32. 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.
  33. Liu, Yu & Chen, Yiming & Jiang, Tao, 2020. "Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach," European Journal of Operational Research, Elsevier, vol. 283(1), pages 166-181.
  34. Yoan Villeneuve & Sara Séguin & Abdellah Chehri, 2023. "AI-Based Scheduling Models, Optimization, and Prediction for Hydropower Generation: Opportunities, Issues, and Future Directions," Energies, MDPI, vol. 16(8), pages 1-27, April.
  35. Athanasios Paraskevas & Dimitrios Aletras & Antonios Chrysopoulos & Antonios Marinopoulos & Dimitrios I. Doukas, 2022. "Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning," Energies, MDPI, vol. 15(7), pages 1-24, March.
  36. Tang, Xiaolin & Zhou, Haitao & Wang, Feng & Wang, Weida & Lin, Xianke, 2022. "Longevity-conscious energy management strategy of fuel cell hybrid electric Vehicle Based on deep reinforcement learning," Energy, Elsevier, vol. 238(PA).
  37. 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.
  38. 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.
  39. Denis Sidorov & Daniil Panasetsky & Nikita Tomin & Dmitriy Karamov & Aleksei Zhukov & Ildar Muftahov & Aliona Dreglea & Fang Liu & Yong Li, 2020. "Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region," Energies, MDPI, vol. 13(5), pages 1-18, March.
  40. Christian Blad & Simon Bøgh & Carsten Kallesøe, 2021. "A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems," Energies, MDPI, vol. 14(22), pages 1-20, November.
  41. Pushan Sharma & Wai Tong Chung & Bassem Akoush & Matthias Ihme, 2023. "A Review of Physics-Informed Machine Learning in Fluid Mechanics," Energies, MDPI, vol. 16(5), pages 1-21, February.
  42. Jaehyun Lee & Eunjung Lee & Jinho Kim, 2020. "Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme," Energies, MDPI, vol. 13(8), pages 1-18, April.
  43. Samende, Cephas & Cao, Jun & Fan, Zhong, 2022. "Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints," Applied Energy, Elsevier, vol. 317(C).
  44. Wang, Hao & He, Hongwen & Bai, Yunfei & Yue, Hongwei, 2022. "Parameterized deep Q-network based energy management with balanced energy economy and battery life for hybrid electric vehicles," Applied Energy, Elsevier, vol. 320(C).
  45. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
  46. Guojian Lin & Wenkai Huang & Chuanshuai Hu & Junlong Xiao & Fobao Zhou & Xiaolin Zhang & Jiajian Liang & Jiaqiao Liang, 2022. "Design and Control Strategy of Soft Robot Based on Gas–Liquid Phase Transition Actuator," Mathematics, MDPI, vol. 10(16), pages 1-20, August.
  47. Cheng, Haoxin & Li, Haihong & Dai, Qionglin & Yang, Junzhong, 2023. "A deep reinforcement learning method to control chaos synchronization between two identical chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
  48. Ondřej Vadinský, 2018. "An Overview of Approaches Evaluating Intelligence of Artificial Systems [Přehled přístupů k vyhodnocování inteligence umělých systémů]," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2018(1), pages 74-103.
  49. Yen, Benjamin P.-C. & Luo, Yu, 2023. "Navigational guidance – A deep learning approach," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1179-1191.
  50. 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).
  51. 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).
  52. 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.
  53. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
  54. Hyungjun Park & Min Kyu Sim & Dong Gu Choi, 2019. "An intelligent financial portfolio trading strategy using deep Q-learning," Papers 1907.03665, arXiv.org, revised Nov 2019.
  55. 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.
  56. Xianhua Peng & Chenyin Gong & Xue Dong He, 2023. "Reinforcement Learning for Financial Index Tracking," Papers 2308.02820, arXiv.org.
  57. Kuldeep Kurte & Jeffrey Munk & Olivera Kotevska & Kadir Amasyali & Robert Smith & Evan McKee & Yan Du & Borui Cui & Teja Kuruganti & Helia Zandi, 2020. "Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
  58. Qin Zhang, 2023. "Dynamic Routing Policies for Multi-Skill Call Centers Using Deep Q Network," Mathematics, MDPI, vol. 11(22), pages 1-18, November.
  59. Yujian Ye & Dawei Qiu & Huiyu Wang & Yi Tang & Goran Strbac, 2021. "Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning," Energies, MDPI, vol. 14(3), pages 1-22, January.
  60. Wang, Xuan & Wang, Rui & Jin, Ming & Shu, Gequn & Tian, Hua & Pan, Jiaying, 2020. "Control of superheat of organic Rankine cycle under transient heat source based on deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
  61. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
  62. Christopher R. Madan, 2020. "Considerations for Comparing Video Game AI Agents with Humans," Challenges, MDPI, vol. 11(2), pages 1-12, August.
  63. 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).
  64. Xiao Liu, 2023. "Dynamic Coupon Targeting Using Batch Deep Reinforcement Learning: An Application to Livestream Shopping," Marketing Science, INFORMS, vol. 42(4), pages 637-658, July.
  65. Mircea-Bogdan Radac & Anamaria-Ioana Borlea, 2021. "Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control," Energies, MDPI, vol. 14(4), pages 1-26, February.
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  70. Alessio Brini & Daniele Tantari, 2021. "Deep Reinforcement Trading with Predictable Returns," Papers 2104.14683, arXiv.org, revised May 2023.
  71. Jian Zhou & Weijian Zheng & Dali Wang & David W. Coit, 2024. "A resilient network recovery framework against cascading failures with deep graph learning," Journal of Risk and Reliability, , vol. 238(1), pages 193-203, February.
  72. Shuai Xue & Hongyang Bai & Daxiang Zhao & Junyan Zhou, 2023. "Research on Intelligent Control Method of Launch Vehicle Landing Based on Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(20), pages 1-17, October.
  73. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
  74. 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.
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  81. Yingfei Wang & Inbal Yahav & Balaji Padmanabhan, 2024. "Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19," Information Systems Research, INFORMS, vol. 35(1), pages 120-144, March.
  82. Xiaomei Zhang & Fan Yang & Qiwen Jin & Ping Lou & Jiwei Hu, 2023. "Path Planning Algorithm for Dual-Arm Robot Based on Depth Deterministic Gradient Strategy Algorithm," Mathematics, MDPI, vol. 11(20), pages 1-19, October.
  83. 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.
  84. 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.
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  91. Zichen Lu & Ying Yan, 2024. "Temperature Control of Fuel Cell Based on PEI-DDPG," Energies, MDPI, vol. 17(7), pages 1-19, April.
  92. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
  93. Ki-Beom Lee & Mohamed A. Ahmed & Dong-Ki Kang & Young-Chon Kim, 2020. "Deep Reinforcement Learning Based Optimal Route and Charging Station Selection," Energies, MDPI, vol. 13(23), pages 1-22, November.
  94. Khalil Bachiri & Ali Yahyaouy & Hamid Gualous & Maria Malek & Younes Bennani & Philippe Makany & Nicoleta Rogovschi, 2023. "Multi-Agent DDPG Based Electric Vehicles Charging Station Recommendation," Energies, MDPI, vol. 16(16), pages 1-17, August.
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  96. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
  97. 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.
  98. Parvez Farazi, Nahid & Zou, Bo & Tulabandhula, Theja, 2022. "Dynamic On-Demand Crowdshipping Using Constrained and Heuristics-Embedded Double Dueling Deep Q-Network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
  99. Yuanfeng Ding & Yan Huang & Li Tang & Xizhong Qin & Zhenhong Jia, 2022. "Resource Allocation in V2X Communications Based on Multi-Agent Reinforcement Learning with Attention Mechanism," Mathematics, MDPI, vol. 10(19), pages 1-19, September.
  100. Zhidong Feng & Ge Liu & Luofeng Wang & Qinghua Gu & Lu Chen, 2023. "Research on the Multiobjective and Efficient Ore-Blending Scheduling of Open-Pit Mines Based on Multiagent Deep Reinforcement Learning," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
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  102. 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.
  103. Weilin Ni & Jiaqi Liu & Zhi Li & Peng Liu & Haizhao Liang, 2023. "Cooperative Guidance Strategy for Active Spacecraft Protection from a Homing Interceptor via Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(19), pages 1-25, October.
  104. Junchi Ma & Yuan Zhang & Zongtao Duan & Lei Tang, 2023. "PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
  105. Muhammad Riza Tanwirul Fuad & Eric Okto Fernandez & Faqihza Mukhlish & Adiyana Putri & Herman Yoseph Sutarto & Yosi Agustina Hidayat & Endra Joelianto, 2022. "Adaptive Deep Q-Network Algorithm with Exponential Reward Mechanism for Traffic Control in Urban Intersection Networks," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
  106. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
  107. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
  108. Ahmed M. Abed & Ali AlArjani, 2022. "The Neural Network Classifier Works Efficiently on Searching in DQN Using the Autonomous Internet of Things Hybridized by the Metaheuristic Techniques to Reduce the EVs’ Service Scheduling Time," Energies, MDPI, vol. 15(19), pages 1-25, September.
  109. 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.
  110. 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).
  111. Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
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