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