Artículos de revistas sobre el tema "Constrained Reinforcement Learning"
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Pankayaraj, Pathmanathan y Pradeep Varakantham. "Constrained Reinforcement Learning in Hard Exploration Problems". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 12 (26 de junio de 2023): 15055–63. http://dx.doi.org/10.1609/aaai.v37i12.26757.
Texto completoHasanzadeZonuzy, Aria, Archana Bura, Dileep Kalathil y Srinivas Shakkottai. "Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 9 (18 de mayo de 2021): 7667–74. http://dx.doi.org/10.1609/aaai.v35i9.16937.
Texto completoDai, Juntao, Jiaming Ji, Long Yang, Qian Zheng y Gang Pan. "Augmented Proximal Policy Optimization for Safe Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junio de 2023): 7288–95. http://dx.doi.org/10.1609/aaai.v37i6.25888.
Texto completoBhatia, Abhinav, Pradeep Varakantham y Akshat Kumar. "Resource Constrained Deep Reinforcement Learning". Proceedings of the International Conference on Automated Planning and Scheduling 29 (25 de mayo de 2021): 610–20. http://dx.doi.org/10.1609/icaps.v29i1.3528.
Texto completoYang, Qisong, Thiago D. Simão, Simon H. Tindemans y Matthijs T. J. Spaan. "WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de mayo de 2021): 10639–46. http://dx.doi.org/10.1609/aaai.v35i12.17272.
Texto completoZhou, Zixian, Mengda Huang, Feiyang Pan, Jia He, Xiang Ao, Dandan Tu y Qing He. "Gradient-Adaptive Pareto Optimization for Constrained Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junio de 2023): 11443–51. http://dx.doi.org/10.1609/aaai.v37i9.26353.
Texto completoHe, Tairan, Weiye Zhao y Changliu Liu. "AutoCost: Evolving Intrinsic Cost for Zero-Violation Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 12 (26 de junio de 2023): 14847–55. http://dx.doi.org/10.1609/aaai.v37i12.26734.
Texto completoYang, Zhaoxing, Haiming Jin, Rong Ding, Haoyi You, Guiyun Fan, Xinbing Wang y Chenghu Zhou. "DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junio de 2023): 10861–70. http://dx.doi.org/10.1609/aaai.v37i9.26288.
Texto completoMartins, Miguel S. E., Joaquim L. Viegas, Tiago Coito, Bernardo Marreiros Firme, João M. C. Sousa, João Figueiredo y Susana M. Vieira. "Reinforcement Learning for Dual-Resource Constrained Scheduling". IFAC-PapersOnLine 53, n.º 2 (2020): 10810–15. http://dx.doi.org/10.1016/j.ifacol.2020.12.2866.
Texto completoGuenter, Florent, Micha Hersch, Sylvain Calinon y Aude Billard. "Reinforcement learning for imitating constrained reaching movements". Advanced Robotics 21, n.º 13 (1 de enero de 2007): 1521–44. http://dx.doi.org/10.1163/156855307782148550.
Texto completoChung, Jen Jen, Nicholas R. J. Lawrance y Salah Sukkarieh. "Learning to soar: Resource-constrained exploration in reinforcement learning". International Journal of Robotics Research 34, n.º 2 (16 de diciembre de 2014): 158–72. http://dx.doi.org/10.1177/0278364914553683.
Texto completoBai, Qinbo, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel y Vaneet Aggarwal. "Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 4 (28 de junio de 2022): 3682–89. http://dx.doi.org/10.1609/aaai.v36i4.20281.
Texto completoBai, Qinbo, Amrit Singh Bedi y Vaneet Aggarwal. "Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junio de 2023): 6737–44. http://dx.doi.org/10.1609/aaai.v37i6.25826.
Texto completoZhao, Hang, Qijin She, Chenyang Zhu, Yin Yang y Kai Xu. "Online 3D Bin Packing with Constrained Deep Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 1 (18 de mayo de 2021): 741–49. http://dx.doi.org/10.1609/aaai.v35i1.16155.
Texto completoPetsagkourakis, P., I. O. Sandoval, E. Bradford, D. Zhang y E. A. del Rio-Chanona. "Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty". IFAC-PapersOnLine 53, n.º 2 (2020): 11264–70. http://dx.doi.org/10.1016/j.ifacol.2020.12.361.
Texto completoPan, Elton, Panagiotis Petsagkourakis, Max Mowbray, Dongda Zhang y Ehecatl Antonio del Rio-Chanona. "Constrained model-free reinforcement learning for process optimization". Computers & Chemical Engineering 154 (noviembre de 2021): 107462. http://dx.doi.org/10.1016/j.compchemeng.2021.107462.
Texto completoGiuseppi, Alessandro y Antonio Pietrabissa. "Chance-Constrained Control With Lexicographic Deep Reinforcement Learning". IEEE Control Systems Letters 4, n.º 3 (julio de 2020): 755–60. http://dx.doi.org/10.1109/lcsys.2020.2979635.
Texto completoGe, Yangyang, Fei Zhu, Wei Huang, Peiyao Zhao y Quan Liu. "Multi-agent cooperation Q-learning algorithm based on constrained Markov Game". Computer Science and Information Systems 17, n.º 2 (2020): 647–64. http://dx.doi.org/10.2298/csis191220009g.
Texto completoFachantidis, Anestis, Matthew Taylor y Ioannis Vlahavas. "Learning to Teach Reinforcement Learning Agents". Machine Learning and Knowledge Extraction 1, n.º 1 (6 de diciembre de 2017): 21–42. http://dx.doi.org/10.3390/make1010002.
Texto completoXu, Yizhen, Zhengyang Zhao, Peng Cheng, Zhuo Chen, Ming Ding, Branka Vucetic y Yonghui Li. "Constrained Reinforcement Learning for Resource Allocation in Network Slicing". IEEE Communications Letters 25, n.º 5 (mayo de 2021): 1554–58. http://dx.doi.org/10.1109/lcomm.2021.3053612.
Texto completoMowbray, M., P. Petsagkourakis, E. A. del Rio-Chanona y D. Zhang. "Safe chance constrained reinforcement learning for batch process control". Computers & Chemical Engineering 157 (enero de 2022): 107630. http://dx.doi.org/10.1016/j.compchemeng.2021.107630.
Texto completoPoznyak, A. S. y K. Najim. "Learning through reinforcement for N-person repeated constrained games". IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, n.º 6 (diciembre de 2002): 759–71. http://dx.doi.org/10.1109/tsmcb.2002.1049610.
Texto completoTsai, Ya-Yen, Bo Xiao, Edward Johns y Guang-Zhong Yang. "Constrained-Space Optimization and Reinforcement Learning for Complex Tasks". IEEE Robotics and Automation Letters 5, n.º 2 (abril de 2020): 683–90. http://dx.doi.org/10.1109/lra.2020.2965392.
Texto completoGao, Yuanqi, Wei Wang, Jie Shi y Nanpeng Yu. "Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration". IEEE Transactions on Smart Grid 11, n.º 6 (noviembre de 2020): 5357–69. http://dx.doi.org/10.1109/tsg.2020.3005270.
Texto completoLin, Wei-Song y Chen-Hong Zheng. "Constrained adaptive optimal control using a reinforcement learning agent". Automatica 48, n.º 10 (octubre de 2012): 2614–19. http://dx.doi.org/10.1016/j.automatica.2012.06.064.
Texto completoHu, Zhenzhen y Wenyin Gong. "Constrained evolutionary optimization based on reinforcement learning using the objective function and constraints". Knowledge-Based Systems 237 (febrero de 2022): 107731. http://dx.doi.org/10.1016/j.knosys.2021.107731.
Texto completoGeibel, P. y F. Wysotzki. "Risk-Sensitive Reinforcement Learning Applied to Control under Constraints". Journal of Artificial Intelligence Research 24 (1 de julio de 2005): 81–108. http://dx.doi.org/10.1613/jair.1666.
Texto completoSzwarcfiter, Claudio, Yale T. Herer y Avraham Shtub. "Balancing Project Schedule, Cost, and Value under Uncertainty: A Reinforcement Learning Approach". Algorithms 16, n.º 8 (21 de agosto de 2023): 395. http://dx.doi.org/10.3390/a16080395.
Texto completoQin, Chunbin, Yinliang Wu, Jishi Zhang y Tianzeng Zhu. "Reinforcement Learning-Based Decentralized Safety Control for Constrained Interconnected Nonlinear Safety-Critical Systems". Entropy 25, n.º 8 (2 de agosto de 2023): 1158. http://dx.doi.org/10.3390/e25081158.
Texto completoDing, Yuhao y Javad Lavaei. "Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junio de 2023): 7396–404. http://dx.doi.org/10.1609/aaai.v37i6.25900.
Texto completoFu, Yanbo, Wenjie Zhao y Liu Liu. "Safe Reinforcement Learning for Transition Control of Ducted-Fan UAVs". Drones 7, n.º 5 (22 de mayo de 2023): 332. http://dx.doi.org/10.3390/drones7050332.
Texto completoWei, Honghao, Xin Liu y Lei Ying. "A Provably-Efficient Model-Free Algorithm for Infinite-Horizon Average-Reward Constrained Markov Decision Processes". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 4 (28 de junio de 2022): 3868–76. http://dx.doi.org/10.1609/aaai.v36i4.20302.
Texto completoQi, Qi, Wenbin Lin, Boyang Guo, Jinshan Chen, Chaoping Deng, Guodong Lin, Xin Sun y Youjia Chen. "Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT". Electronics 11, n.º 20 (19 de octubre de 2022): 3385. http://dx.doi.org/10.3390/electronics11203385.
Texto completoPocius, Rey, Lawrence Neal y Alan Fern. "Strategic Tasks for Explainable Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 10007–8. http://dx.doi.org/10.1609/aaai.v33i01.330110007.
Texto completoDinu, Alexandru y Petre Lucian Ogrutan. "Reinforcement Learning Made Affordable for Hardware Verification Engineers". Micromachines 13, n.º 11 (1 de noviembre de 2022): 1887. http://dx.doi.org/10.3390/mi13111887.
Texto completoBrosowsky, Mathis, Florian Keck, Olaf Dünkel y Marius Zöllner. "Sample-Specific Output Constraints for Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de mayo de 2021): 6812–21. http://dx.doi.org/10.1609/aaai.v35i8.16841.
Texto completoParsonson, Christopher W. F., Alexandre Laterre y Thomas D. Barrett. "Reinforcement Learning for Branch-and-Bound Optimisation Using Retrospective Trajectories". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 4 (26 de junio de 2023): 4061–69. http://dx.doi.org/10.1609/aaai.v37i4.25521.
Texto completoBrázdil, Tomáš, Krishnendu Chatterjee, Petr Novotný y Jiří Vahala. "Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 06 (3 de abril de 2020): 9794–801. http://dx.doi.org/10.1609/aaai.v34i06.6531.
Texto completoZhang, Hongchang, Jianzhun Shao, Yuhang Jiang, Shuncheng He, Guanwen Zhang y Xiangyang Ji. "State Deviation Correction for Offline Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junio de 2022): 9022–30. http://dx.doi.org/10.1609/aaai.v36i8.20886.
Texto completoBai, Fengshuo, Hongming Zhang, Tianyang Tao, Zhiheng Wu, Yanna Wang y Bo Xu. "PiCor: Multi-Task Deep Reinforcement Learning with Policy Correction". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junio de 2023): 6728–36. http://dx.doi.org/10.1609/aaai.v37i6.25825.
Texto completoLee, Xian Yeow, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde y Soumik Sarkar. "Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 4577–84. http://dx.doi.org/10.1609/aaai.v34i04.5887.
Texto completoCostero, Luis, Arman Iranfar, Marina Zapater, Francisco D. Igual, Katzalin Olcoz y David Atienza. "Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning". IEEE Transactions on Parallel and Distributed Systems 31, n.º 12 (1 de diciembre de 2020): 2834–50. http://dx.doi.org/10.1109/tpds.2020.3004735.
Texto completoUchibe, Eiji y Kenji Doya. "Finding intrinsic rewards by embodied evolution and constrained reinforcement learning". Neural Networks 21, n.º 10 (diciembre de 2008): 1447–55. http://dx.doi.org/10.1016/j.neunet.2008.09.013.
Texto completoLi, Hepeng, Zhiqiang Wan y Haibo He. "Constrained EV Charging Scheduling Based on Safe Deep Reinforcement Learning". IEEE Transactions on Smart Grid 11, n.º 3 (mayo de 2020): 2427–39. http://dx.doi.org/10.1109/tsg.2019.2955437.
Texto completoWang, Huiwei, Tingwen Huang, Xiaofeng Liao, Haitham Abu-Rub y Guo Chen. "Reinforcement Learning for Constrained Energy Trading Games With Incomplete Information". IEEE Transactions on Cybernetics 47, n.º 10 (octubre de 2017): 3404–16. http://dx.doi.org/10.1109/tcyb.2016.2539300.
Texto completoDong, Wenbo, Shaofan Liu y Shiliang Sun. "Safe batch constrained deep reinforcement learning with generative adversarial network". Information Sciences 634 (julio de 2023): 259–70. http://dx.doi.org/10.1016/j.ins.2023.03.108.
Texto completoKorivand, Soroush, Nader Jalili y Jiaqi Gong. "Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling". Sensors 23, n.º 5 (1 de marzo de 2023): 2698. http://dx.doi.org/10.3390/s23052698.
Texto completoJing, Mingxuan, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Chao Yang, Bin Fang y Huaping Liu. "Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5109–16. http://dx.doi.org/10.1609/aaai.v34i04.5953.
Texto completoMa, Jing, So Hasegawa, Song-Ju Kim y Mikio Hasegawa. "A Reinforcement-Learning-Based Distributed Resource Selection Algorithm for Massive IoT". Applied Sciences 9, n.º 18 (6 de septiembre de 2019): 3730. http://dx.doi.org/10.3390/app9183730.
Texto completoDing, Zhenhuan, Xiaoge Huang y Zhao Liu. "Active Exploration by Chance-Constrained Optimization for Voltage Regulation with Reinforcement Learning". Energies 15, n.º 2 (16 de enero de 2022): 614. http://dx.doi.org/10.3390/en15020614.
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