Artigos de revistas sobre o tema "Actor-critic methods"
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Parisi, Simone, Voot Tangkaratt, Jan Peters e Mohammad Emtiyaz Khan. "TD-regularized actor-critic methods". Machine Learning 108, n.º 8-9 (21 de fevereiro de 2019): 1467–501. http://dx.doi.org/10.1007/s10994-019-05788-0.
Texto completo da fonteWang, Jing, Xuchu Ding, Morteza Lahijanian, Ioannis Ch Paschalidis e Calin A. Belta. "Temporal logic motion control using actor–critic methods". International Journal of Robotics Research 34, n.º 10 (26 de maio de 2015): 1329–44. http://dx.doi.org/10.1177/0278364915581505.
Texto completo da fonteGrondman, I., M. Vaandrager, L. Busoniu, R. Babuska e E. Schuitema. "Efficient Model Learning Methods for Actor–Critic Control". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42, n.º 3 (junho de 2012): 591–602. http://dx.doi.org/10.1109/tsmcb.2011.2170565.
Texto completo da fonteWang, Mingyi, Jianhao Tang, Haoli Zhao, Zhenni Li e Shengli Xie. "Automatic Compression of Neural Network with Deep Reinforcement Learning Based on Proximal Gradient Method". Mathematics 11, n.º 2 (9 de janeiro de 2023): 338. http://dx.doi.org/10.3390/math11020338.
Texto completo da fonteSu, Jianyu, Stephen Adams e Peter Beling. "Value-Decomposition Multi-Agent Actor-Critics". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de maio de 2021): 11352–60. http://dx.doi.org/10.1609/aaai.v35i13.17353.
Texto completo da fonteSaglam, Baturay, Furkan B. Mutlu, Dogan C. Cicek e Suleyman S. Kozat. "Actor Prioritized Experience Replay". Journal of Artificial Intelligence Research 78 (16 de novembro de 2023): 639–72. http://dx.doi.org/10.1613/jair.1.14819.
Texto completo da fonteSeo, Kanghyeon, e Jihoon Yang. "Differentially Private Actor and Its Eligibility Trace". Electronics 9, n.º 9 (10 de setembro de 2020): 1486. http://dx.doi.org/10.3390/electronics9091486.
Texto completo da fonteSaglam, Baturay, Furkan Mutlu, Dogan Cicek e Suleyman Kozat. "Actor Prioritized Experience Replay (Abstract Reprint)". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 20 (24 de março de 2024): 22710. http://dx.doi.org/10.1609/aaai.v38i20.30610.
Texto completo da fonteHafez, Muhammad Burhan, Cornelius Weber, Matthias Kerzel e Stefan Wermter. "Deep intrinsically motivated continuous actor-critic for efficient robotic visuomotor skill learning". Paladyn, Journal of Behavioral Robotics 10, n.º 1 (1 de janeiro de 2019): 14–29. http://dx.doi.org/10.1515/pjbr-2019-0005.
Texto completo da fonteKong, Minseok, e Jungmin So. "Empirical Analysis of Automated Stock Trading Using Deep Reinforcement Learning". Applied Sciences 13, n.º 1 (3 de janeiro de 2023): 633. http://dx.doi.org/10.3390/app13010633.
Texto completo da fonteHernandez-Leal, Pablo, Bilal Kartal e Matthew E. Taylor. "Agent Modeling as Auxiliary Task for Deep Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 15, n.º 1 (8 de outubro de 2019): 31–37. http://dx.doi.org/10.1609/aiide.v15i1.5221.
Texto completo da fonteArvindhan, M., e D. Rajesh Kumar. "Adaptive Resource Allocation in Cloud Data Centers using Actor-Critical Deep Reinforcement Learning for Optimized Load Balancing". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 5s (18 de maio de 2023): 310–18. http://dx.doi.org/10.17762/ijritcc.v11i5s.6671.
Texto completo da fonteAws, Ahmad, Arkadij Yuschenko e Vladimir Soloviev. "End-to-end deep reinforcement learning for control of an autonomous underwater robot with an undulating propulsor". Robotics and Technical Cybernetics 12, n.º 1 (março de 2024): 36–45. http://dx.doi.org/10.31776/rtcj.12105.
Texto completo da fonteZhang, Haifeng, Weizhe Chen, Zeren Huang, Minne Li, Yaodong Yang, Weinan Zhang e Jun Wang. "Bi-Level Actor-Critic for Multi-Agent Coordination". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 05 (3 de abril de 2020): 7325–32. http://dx.doi.org/10.1609/aaai.v34i05.6226.
Texto completo da fonteLuo, Ziwei, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song, Xi Wu e Siwei Lyu. "Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 2 (28 de junho de 2022): 1917–25. http://dx.doi.org/10.1609/aaai.v36i2.20086.
Texto completo da fonteAslani, Mohammad, Mohammad Saadi Mesgari, Stefan Seipel e Marco Wiering. "Developing adaptive traffic signal control by actor–critic and direct exploration methods". Proceedings of the Institution of Civil Engineers - Transport 172, n.º 5 (outubro de 2019): 289–98. http://dx.doi.org/10.1680/jtran.17.00085.
Texto completo da fonteDoya, Kenji. "Reinforcement Learning in Continuous Time and Space". Neural Computation 12, n.º 1 (1 de janeiro de 2000): 219–45. http://dx.doi.org/10.1162/089976600300015961.
Texto completo da fonteZhu, Qingling, Xiaoqiang Wu, Qiuzhen Lin e Wei-Neng Chen. "Two-Stage Evolutionary Reinforcement Learning for Enhancing Exploration and Exploitation". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 18 (24 de março de 2024): 20892–900. http://dx.doi.org/10.1609/aaai.v38i18.30079.
Texto completo da fonteJain, Arushi, Gandharv Patil, Ayush Jain, Khimya Khetarpal e Doina Precup. "Variance Penalized On-Policy and Off-Policy Actor-Critic". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 9 (18 de maio de 2021): 7899–907. http://dx.doi.org/10.1609/aaai.v35i9.16964.
Texto completo da fonteRyu, Heechang, Hayong Shin e Jinkyoo Park. "Multi-Agent Actor-Critic with Hierarchical Graph Attention Network". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 05 (3 de abril de 2020): 7236–43. http://dx.doi.org/10.1609/aaai.v34i05.6214.
Texto completo da fonteShi, Daming, Xudong Guo, Yi Liu e Wenhui Fan. "Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning". Entropy 24, n.º 6 (30 de maio de 2022): 774. http://dx.doi.org/10.3390/e24060774.
Texto completo da fonteWang, Hui, Peng Zhang e Quan Liu. "An Actor-critic Algorithm Using Cross Evaluation of Value Functions". IAES International Journal of Robotics and Automation (IJRA) 7, n.º 1 (1 de março de 2018): 39. http://dx.doi.org/10.11591/ijra.v7i1.pp39-47.
Texto completo da fonteZhang, Zuozhen, Junzhong Ji e Jinduo Liu. "MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 9 (24 de março de 2024): 10261–69. http://dx.doi.org/10.1609/aaai.v38i9.28892.
Texto completo da fonteZhao, Nan, Zehua Liu, Yiqiang Cheng e Chao Tian. "Multi-Agent Actor Critic for Channel Allocation in Heterogeneous Networks". International Journal of Mobile Computing and Multimedia Communications 11, n.º 1 (janeiro de 2020): 23–41. http://dx.doi.org/10.4018/ijmcmc.2020010102.
Texto completo da fonteChen, Haibo, Zhongwei Huang, Xiaorong Zhao, Xiao Liu, Youjun Jiang, Pinyong Geng, Guang Yang, Yewen Cao e Deqiang Wang. "Policy Optimization of the Power Allocation Algorithm Based on the Actor–Critic Framework in Small Cell Networks". Mathematics 11, n.º 7 (2 de abril de 2023): 1702. http://dx.doi.org/10.3390/math11071702.
Texto completo da fonteYang, Qisong, Thiago D. Simão, Simon H. Tindemans e 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 maio de 2021): 10639–46. http://dx.doi.org/10.1609/aaai.v35i12.17272.
Texto completo da fonteWang, Zhihai, Jie Wang, Qi Zhou, Bin Li e Houqiang Li. "Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junho de 2022): 8612–20. http://dx.doi.org/10.1609/aaai.v36i8.20839.
Texto completo da fonteZhong, Shan, Quan Liu e QiMing Fu. "Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning". Computational Intelligence and Neuroscience 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/4824072.
Texto completo da fonteWu, Zhenning, Yiming Deng e Lixing Wang. "A Pinning Actor-Critic Structure-Based Algorithm for Sizing Complex-Shaped Depth Profiles in MFL Inspection with High Degree of Freedom". Complexity 2021 (23 de abril de 2021): 1–12. http://dx.doi.org/10.1155/2021/9995033.
Texto completo da fonteLiang, Kun, Guoqiang Zhang, Jinhui Guo e Wentao Li. "An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation". Electronics 12, n.º 24 (8 de dezembro de 2023): 4939. http://dx.doi.org/10.3390/electronics12244939.
Texto completo da fonteKwon, Ki-Young, Keun-Woo Jung, Dong-Su Yang e Jooyoung Park. "Autonomous Vehicle Path Tracking Based on Natural Gradient Methods". Journal of Advanced Computational Intelligence and Intelligent Informatics 16, n.º 7 (20 de novembro de 2012): 888–93. http://dx.doi.org/10.20965/jaciii.2012.p0888.
Texto completo da fonteLi, Yarong. "Sequence Alignment with Q-Learning Based on the Actor-Critic Model". ACM Transactions on Asian and Low-Resource Language Information Processing 20, n.º 5 (2 de julho de 2021): 1–7. http://dx.doi.org/10.1145/3433540.
Texto completo da fonteJiang, Liang, Ying Nan, Yu Zhang e Zhihan Li. "Anti-Interception Guidance for Hypersonic Glide Vehicle: A Deep Reinforcement Learning Approach". Aerospace 9, n.º 8 (4 de agosto de 2022): 424. http://dx.doi.org/10.3390/aerospace9080424.
Texto completo da fonteLikmeta, Amarildo, Matteo Sacco, Alberto Maria Metelli e Marcello Restelli. "Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junho de 2023): 8782–90. http://dx.doi.org/10.1609/aaai.v37i7.26056.
Texto completo da fonteShi, Lei, Tian Li, Lin Wei, Yongcai Tao, Cuixia Li e Yufei Gao. "FASTune: Towards Fast and Stable Database Tuning System with Reinforcement Learning". Electronics 12, n.º 10 (10 de maio de 2023): 2168. http://dx.doi.org/10.3390/electronics12102168.
Texto completo da fonteYu, Zhiwen, Wenjie Zheng, Kaiwen Zeng, Ruifeng Zhao, Yanxu Zhang e Mengdi Zeng. "Energy optimization management of microgrid using improved soft actor-critic algorithm". International Journal of Renewable Energy Development 13, n.º 2 (20 de fevereiro de 2024): 329–39. http://dx.doi.org/10.61435/ijred.2024.59988.
Texto completo da fonteIsmail, Ahmed, e Mustafa Baysal. "Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning". Energies 16, n.º 14 (19 de julho de 2023): 5469. http://dx.doi.org/10.3390/en16145469.
Texto completo da fonteDrechsler, M. Funk, T. A. Fiorentin e H. Göllinger. "Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training". Modelling and Simulation in Engineering 2021 (7 de dezembro de 2021): 1–10. http://dx.doi.org/10.1155/2021/4641450.
Texto completo da fonteWu, Jiying, Zhong Yang, Haoze Zhuo, Changliang Xu, Chi Zhang, Naifeng He, Luwei Liao e Zhiyong Wang. "A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering". Drones 8, n.º 3 (20 de fevereiro de 2024): 69. http://dx.doi.org/10.3390/drones8030069.
Texto completo da fonteQian, Tiancheng, Xue Mei, Pengxiang Xu, Kangqi Ge e Zhelei Qiu. "Filtration network: A frame sampling strategy via deep reinforcement learning for video captioning". Journal of Intelligent & Fuzzy Systems 40, n.º 6 (21 de junho de 2021): 11085–97. http://dx.doi.org/10.3233/jifs-202249.
Texto completo da fonteWang, Xinshui, Ke Meng, Xu Wang, Zhibin Liu e Yuefeng Ma. "Dynamic User Resource Allocation for Downlink Multicarrier NOMA with an Actor–Critic Method". Energies 16, n.º 7 (24 de março de 2023): 2984. http://dx.doi.org/10.3390/en16072984.
Texto completo da fonteMelo, Francisco. "Differential Eligibility Vectors for Advantage Updating and Gradient Methods". Proceedings of the AAAI Conference on Artificial Intelligence 25, n.º 1 (4 de agosto de 2011): 441–46. http://dx.doi.org/10.1609/aaai.v25i1.7938.
Texto completo da fonteLyu, Xueguang, Andrea Baisero, Yuchen Xiao, Brett Daley e Christopher Amato. "On Centralized Critics in Multi-Agent Reinforcement Learning". Journal of Artificial Intelligence Research 77 (31 de maio de 2023): 295–354. http://dx.doi.org/10.1613/jair.1.14386.
Texto completo da fonteZhao, Mingjun, Haijiang Wu, Di Niu e Xiaoli Wang. "Reinforced Curriculum Learning on Pre-Trained Neural Machine Translation Models". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 05 (3 de abril de 2020): 9652–59. http://dx.doi.org/10.1609/aaai.v34i05.6513.
Texto completo da fonteZhao, Jun, Qingliang Zeng e Bin Guo. "Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics". Computational Intelligence and Neuroscience 2021 (16 de novembro de 2021): 1–8. http://dx.doi.org/10.1155/2021/2952115.
Texto completo da fonteYue, Longfei, Rennong Yang, Jialiang Zuo, Mengda Yan, Xiaoru Zhao e Maolong Lv. "Factored Multi-Agent Soft Actor-Critic for Cooperative Multi-Target Tracking of UAV Swarms". Drones 7, n.º 3 (22 de fevereiro de 2023): 150. http://dx.doi.org/10.3390/drones7030150.
Texto completo da fonteZhou, Kun, Wenyong Wang, Teng Hu e Kai Deng. "Application of Improved Asynchronous Advantage Actor Critic Reinforcement Learning Model on Anomaly Detection". Entropy 23, n.º 3 (25 de fevereiro de 2021): 274. http://dx.doi.org/10.3390/e23030274.
Texto completo da fonteLu, Junqi, Xinning Wu, Su Cao, Xiangke Wang e Huangchao Yu. "An Implementation of Actor-Critic Algorithm on Spiking Neural Network Using Temporal Coding Method". Applied Sciences 12, n.º 20 (16 de outubro de 2022): 10430. http://dx.doi.org/10.3390/app122010430.
Texto completo da fonteOh, Sang Ho, Jeongyoon Kim, Jae Hoon Nah e Jongyoul Park. "Employing Deep Reinforcement Learning to Cyber-Attack Simulation for Enhancing Cybersecurity". Electronics 13, n.º 3 (30 de janeiro de 2024): 555. http://dx.doi.org/10.3390/electronics13030555.
Texto completo da fonteSun, Zhiyao, e Guifen Chen. "Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching". Electronics 13, n.º 4 (18 de fevereiro de 2024): 794. http://dx.doi.org/10.3390/electronics13040794.
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