Artykuły w czasopismach na temat „Actor-critic methods”
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Parisi, Simone, Voot Tangkaratt, Jan Peters i Mohammad Emtiyaz Khan. "TD-regularized actor-critic methods". Machine Learning 108, nr 8-9 (21.02.2019): 1467–501. http://dx.doi.org/10.1007/s10994-019-05788-0.
Pełny tekst źródłaWang, Jing, Xuchu Ding, Morteza Lahijanian, Ioannis Ch Paschalidis i Calin A. Belta. "Temporal logic motion control using actor–critic methods". International Journal of Robotics Research 34, nr 10 (26.05.2015): 1329–44. http://dx.doi.org/10.1177/0278364915581505.
Pełny tekst źródłaGrondman, I., M. Vaandrager, L. Busoniu, R. Babuska i E. Schuitema. "Efficient Model Learning Methods for Actor–Critic Control". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42, nr 3 (czerwiec 2012): 591–602. http://dx.doi.org/10.1109/tsmcb.2011.2170565.
Pełny tekst źródłaWang, Mingyi, Jianhao Tang, Haoli Zhao, Zhenni Li i Shengli Xie. "Automatic Compression of Neural Network with Deep Reinforcement Learning Based on Proximal Gradient Method". Mathematics 11, nr 2 (9.01.2023): 338. http://dx.doi.org/10.3390/math11020338.
Pełny tekst źródłaSu, Jianyu, Stephen Adams i Peter Beling. "Value-Decomposition Multi-Agent Actor-Critics". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 13 (18.05.2021): 11352–60. http://dx.doi.org/10.1609/aaai.v35i13.17353.
Pełny tekst źródłaSaglam, Baturay, Furkan B. Mutlu, Dogan C. Cicek i Suleyman S. Kozat. "Actor Prioritized Experience Replay". Journal of Artificial Intelligence Research 78 (16.11.2023): 639–72. http://dx.doi.org/10.1613/jair.1.14819.
Pełny tekst źródłaSeo, Kanghyeon, i Jihoon Yang. "Differentially Private Actor and Its Eligibility Trace". Electronics 9, nr 9 (10.09.2020): 1486. http://dx.doi.org/10.3390/electronics9091486.
Pełny tekst źródłaSaglam, Baturay, Furkan Mutlu, Dogan Cicek i Suleyman Kozat. "Actor Prioritized Experience Replay (Abstract Reprint)". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 20 (24.03.2024): 22710. http://dx.doi.org/10.1609/aaai.v38i20.30610.
Pełny tekst źródłaHafez, Muhammad Burhan, Cornelius Weber, Matthias Kerzel i Stefan Wermter. "Deep intrinsically motivated continuous actor-critic for efficient robotic visuomotor skill learning". Paladyn, Journal of Behavioral Robotics 10, nr 1 (1.01.2019): 14–29. http://dx.doi.org/10.1515/pjbr-2019-0005.
Pełny tekst źródłaKong, Minseok, i Jungmin So. "Empirical Analysis of Automated Stock Trading Using Deep Reinforcement Learning". Applied Sciences 13, nr 1 (3.01.2023): 633. http://dx.doi.org/10.3390/app13010633.
Pełny tekst źródłaHernandez-Leal, Pablo, Bilal Kartal i 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, nr 1 (8.10.2019): 31–37. http://dx.doi.org/10.1609/aiide.v15i1.5221.
Pełny tekst źródłaArvindhan, M., i 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, nr 5s (18.05.2023): 310–18. http://dx.doi.org/10.17762/ijritcc.v11i5s.6671.
Pełny tekst źródłaAws, Ahmad, Arkadij Yuschenko i Vladimir Soloviev. "End-to-end deep reinforcement learning for control of an autonomous underwater robot with an undulating propulsor". Robotics and Technical Cybernetics 12, nr 1 (marzec 2024): 36–45. http://dx.doi.org/10.31776/rtcj.12105.
Pełny tekst źródłaZhang, Haifeng, Weizhe Chen, Zeren Huang, Minne Li, Yaodong Yang, Weinan Zhang i Jun Wang. "Bi-Level Actor-Critic for Multi-Agent Coordination". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 05 (3.04.2020): 7325–32. http://dx.doi.org/10.1609/aaai.v34i05.6226.
Pełny tekst źródłaLuo, Ziwei, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song, Xi Wu i Siwei Lyu. "Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 2 (28.06.2022): 1917–25. http://dx.doi.org/10.1609/aaai.v36i2.20086.
Pełny tekst źródłaAslani, Mohammad, Mohammad Saadi Mesgari, Stefan Seipel i Marco Wiering. "Developing adaptive traffic signal control by actor–critic and direct exploration methods". Proceedings of the Institution of Civil Engineers - Transport 172, nr 5 (październik 2019): 289–98. http://dx.doi.org/10.1680/jtran.17.00085.
Pełny tekst źródłaDoya, Kenji. "Reinforcement Learning in Continuous Time and Space". Neural Computation 12, nr 1 (1.01.2000): 219–45. http://dx.doi.org/10.1162/089976600300015961.
Pełny tekst źródłaZhu, Qingling, Xiaoqiang Wu, Qiuzhen Lin i Wei-Neng Chen. "Two-Stage Evolutionary Reinforcement Learning for Enhancing Exploration and Exploitation". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 18 (24.03.2024): 20892–900. http://dx.doi.org/10.1609/aaai.v38i18.30079.
Pełny tekst źródłaJain, Arushi, Gandharv Patil, Ayush Jain, Khimya Khetarpal i Doina Precup. "Variance Penalized On-Policy and Off-Policy Actor-Critic". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 9 (18.05.2021): 7899–907. http://dx.doi.org/10.1609/aaai.v35i9.16964.
Pełny tekst źródłaRyu, Heechang, Hayong Shin i Jinkyoo Park. "Multi-Agent Actor-Critic with Hierarchical Graph Attention Network". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 05 (3.04.2020): 7236–43. http://dx.doi.org/10.1609/aaai.v34i05.6214.
Pełny tekst źródłaShi, Daming, Xudong Guo, Yi Liu i Wenhui Fan. "Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning". Entropy 24, nr 6 (30.05.2022): 774. http://dx.doi.org/10.3390/e24060774.
Pełny tekst źródłaWang, Hui, Peng Zhang i Quan Liu. "An Actor-critic Algorithm Using Cross Evaluation of Value Functions". IAES International Journal of Robotics and Automation (IJRA) 7, nr 1 (1.03.2018): 39. http://dx.doi.org/10.11591/ijra.v7i1.pp39-47.
Pełny tekst źródłaZhang, Zuozhen, Junzhong Ji i Jinduo Liu. "MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 9 (24.03.2024): 10261–69. http://dx.doi.org/10.1609/aaai.v38i9.28892.
Pełny tekst źródłaZhao, Nan, Zehua Liu, Yiqiang Cheng i Chao Tian. "Multi-Agent Actor Critic for Channel Allocation in Heterogeneous Networks". International Journal of Mobile Computing and Multimedia Communications 11, nr 1 (styczeń 2020): 23–41. http://dx.doi.org/10.4018/ijmcmc.2020010102.
Pełny tekst źródłaChen, Haibo, Zhongwei Huang, Xiaorong Zhao, Xiao Liu, Youjun Jiang, Pinyong Geng, Guang Yang, Yewen Cao i Deqiang Wang. "Policy Optimization of the Power Allocation Algorithm Based on the Actor–Critic Framework in Small Cell Networks". Mathematics 11, nr 7 (2.04.2023): 1702. http://dx.doi.org/10.3390/math11071702.
Pełny tekst źródłaYang, Qisong, Thiago D. Simão, Simon H. Tindemans i Matthijs T. J. Spaan. "WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 12 (18.05.2021): 10639–46. http://dx.doi.org/10.1609/aaai.v35i12.17272.
Pełny tekst źródłaWang, Zhihai, Jie Wang, Qi Zhou, Bin Li i Houqiang Li. "Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 8 (28.06.2022): 8612–20. http://dx.doi.org/10.1609/aaai.v36i8.20839.
Pełny tekst źródłaZhong, Shan, Quan Liu i 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.
Pełny tekst źródłaWu, Zhenning, Yiming Deng i 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 (23.04.2021): 1–12. http://dx.doi.org/10.1155/2021/9995033.
Pełny tekst źródłaLiang, Kun, Guoqiang Zhang, Jinhui Guo i Wentao Li. "An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation". Electronics 12, nr 24 (8.12.2023): 4939. http://dx.doi.org/10.3390/electronics12244939.
Pełny tekst źródłaKwon, Ki-Young, Keun-Woo Jung, Dong-Su Yang i Jooyoung Park. "Autonomous Vehicle Path Tracking Based on Natural Gradient Methods". Journal of Advanced Computational Intelligence and Intelligent Informatics 16, nr 7 (20.11.2012): 888–93. http://dx.doi.org/10.20965/jaciii.2012.p0888.
Pełny tekst źródłaLi, Yarong. "Sequence Alignment with Q-Learning Based on the Actor-Critic Model". ACM Transactions on Asian and Low-Resource Language Information Processing 20, nr 5 (2.07.2021): 1–7. http://dx.doi.org/10.1145/3433540.
Pełny tekst źródłaJiang, Liang, Ying Nan, Yu Zhang i Zhihan Li. "Anti-Interception Guidance for Hypersonic Glide Vehicle: A Deep Reinforcement Learning Approach". Aerospace 9, nr 8 (4.08.2022): 424. http://dx.doi.org/10.3390/aerospace9080424.
Pełny tekst źródłaLikmeta, Amarildo, Matteo Sacco, Alberto Maria Metelli i Marcello Restelli. "Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 7 (26.06.2023): 8782–90. http://dx.doi.org/10.1609/aaai.v37i7.26056.
Pełny tekst źródłaShi, Lei, Tian Li, Lin Wei, Yongcai Tao, Cuixia Li i Yufei Gao. "FASTune: Towards Fast and Stable Database Tuning System with Reinforcement Learning". Electronics 12, nr 10 (10.05.2023): 2168. http://dx.doi.org/10.3390/electronics12102168.
Pełny tekst źródłaYu, Zhiwen, Wenjie Zheng, Kaiwen Zeng, Ruifeng Zhao, Yanxu Zhang i Mengdi Zeng. "Energy optimization management of microgrid using improved soft actor-critic algorithm". International Journal of Renewable Energy Development 13, nr 2 (20.02.2024): 329–39. http://dx.doi.org/10.61435/ijred.2024.59988.
Pełny tekst źródłaIsmail, Ahmed, i Mustafa Baysal. "Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning". Energies 16, nr 14 (19.07.2023): 5469. http://dx.doi.org/10.3390/en16145469.
Pełny tekst źródłaDrechsler, M. Funk, T. A. Fiorentin i H. Göllinger. "Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training". Modelling and Simulation in Engineering 2021 (7.12.2021): 1–10. http://dx.doi.org/10.1155/2021/4641450.
Pełny tekst źródłaWu, Jiying, Zhong Yang, Haoze Zhuo, Changliang Xu, Chi Zhang, Naifeng He, Luwei Liao i Zhiyong Wang. "A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering". Drones 8, nr 3 (20.02.2024): 69. http://dx.doi.org/10.3390/drones8030069.
Pełny tekst źródłaQian, Tiancheng, Xue Mei, Pengxiang Xu, Kangqi Ge i Zhelei Qiu. "Filtration network: A frame sampling strategy via deep reinforcement learning for video captioning". Journal of Intelligent & Fuzzy Systems 40, nr 6 (21.06.2021): 11085–97. http://dx.doi.org/10.3233/jifs-202249.
Pełny tekst źródłaWang, Xinshui, Ke Meng, Xu Wang, Zhibin Liu i Yuefeng Ma. "Dynamic User Resource Allocation for Downlink Multicarrier NOMA with an Actor–Critic Method". Energies 16, nr 7 (24.03.2023): 2984. http://dx.doi.org/10.3390/en16072984.
Pełny tekst źródłaMelo, Francisco. "Differential Eligibility Vectors for Advantage Updating and Gradient Methods". Proceedings of the AAAI Conference on Artificial Intelligence 25, nr 1 (4.08.2011): 441–46. http://dx.doi.org/10.1609/aaai.v25i1.7938.
Pełny tekst źródłaLyu, Xueguang, Andrea Baisero, Yuchen Xiao, Brett Daley i Christopher Amato. "On Centralized Critics in Multi-Agent Reinforcement Learning". Journal of Artificial Intelligence Research 77 (31.05.2023): 295–354. http://dx.doi.org/10.1613/jair.1.14386.
Pełny tekst źródłaZhao, Mingjun, Haijiang Wu, Di Niu i Xiaoli Wang. "Reinforced Curriculum Learning on Pre-Trained Neural Machine Translation Models". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 05 (3.04.2020): 9652–59. http://dx.doi.org/10.1609/aaai.v34i05.6513.
Pełny tekst źródłaZhao, Jun, Qingliang Zeng i Bin Guo. "Adaptive Critic Learning-Based Robust Control of Systems with Uncertain Dynamics". Computational Intelligence and Neuroscience 2021 (16.11.2021): 1–8. http://dx.doi.org/10.1155/2021/2952115.
Pełny tekst źródłaYue, Longfei, Rennong Yang, Jialiang Zuo, Mengda Yan, Xiaoru Zhao i Maolong Lv. "Factored Multi-Agent Soft Actor-Critic for Cooperative Multi-Target Tracking of UAV Swarms". Drones 7, nr 3 (22.02.2023): 150. http://dx.doi.org/10.3390/drones7030150.
Pełny tekst źródłaZhou, Kun, Wenyong Wang, Teng Hu i Kai Deng. "Application of Improved Asynchronous Advantage Actor Critic Reinforcement Learning Model on Anomaly Detection". Entropy 23, nr 3 (25.02.2021): 274. http://dx.doi.org/10.3390/e23030274.
Pełny tekst źródłaLu, Junqi, Xinning Wu, Su Cao, Xiangke Wang i Huangchao Yu. "An Implementation of Actor-Critic Algorithm on Spiking Neural Network Using Temporal Coding Method". Applied Sciences 12, nr 20 (16.10.2022): 10430. http://dx.doi.org/10.3390/app122010430.
Pełny tekst źródłaOh, Sang Ho, Jeongyoon Kim, Jae Hoon Nah i Jongyoul Park. "Employing Deep Reinforcement Learning to Cyber-Attack Simulation for Enhancing Cybersecurity". Electronics 13, nr 3 (30.01.2024): 555. http://dx.doi.org/10.3390/electronics13030555.
Pełny tekst źródłaSun, Zhiyao, i Guifen Chen. "Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching". Electronics 13, nr 4 (18.02.2024): 794. http://dx.doi.org/10.3390/electronics13040794.
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