Artykuły w czasopismach na temat „Sparsely rewarded environments”
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Dubey, Rachit, Thomas L. Griffiths i Peter Dayan. "The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons". PLOS Computational Biology 18, nr 8 (4.08.2022): e1010316. http://dx.doi.org/10.1371/journal.pcbi.1010316.
Pełny tekst źródłaShi, Xiaoping, Shiqi Zou, Shenmin Song i Rui Guo. "A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy". Journal of Intelligent & Fuzzy Systems 40, nr 5 (22.04.2021): 10043–61. http://dx.doi.org/10.3233/jifs-202679.
Pełny tekst źródłaSakamoto, Yuma, i Kentarou Kurashige. "Self-Generating Evaluations for Robot’s Autonomy Based on Sensor Input". Machines 11, nr 9 (6.09.2023): 892. http://dx.doi.org/10.3390/machines11090892.
Pełny tekst źródłaParisi, Simone, Davide Tateo, Maximilian Hensel, Carlo D’Eramo, Jan Peters i Joni Pajarinen. "Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning". Algorithms 15, nr 3 (28.02.2022): 81. http://dx.doi.org/10.3390/a15030081.
Pełny tekst źródłaMguni, David, Taher Jafferjee, Jianhong Wang, Nicolas Perez-Nieves, Wenbin Song, Feifei Tong, Matthew Taylor i in. "Learning to Shape Rewards Using a Game of Two Partners". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 10 (26.06.2023): 11604–12. http://dx.doi.org/10.1609/aaai.v37i10.26371.
Pełny tekst źródłaForbes, Grant C., i David L. Roberts. "Potential-Based Reward Shaping for Intrinsic Motivation (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 21 (24.03.2024): 23488–89. http://dx.doi.org/10.1609/aaai.v38i21.30441.
Pełny tekst źródłaXu, Pei, Junge Zhang, Qiyue Yin, Chao Yu, Yaodong Yang i Kaiqi Huang. "Subspace-Aware Exploration for Sparse-Reward Multi-Agent Tasks". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 10 (26.06.2023): 11717–25. http://dx.doi.org/10.1609/aaai.v37i10.26384.
Pełny tekst źródłaKubovčík, Martin, Iveta Dirgová Luptáková i Jiří Pospíchal. "Signal Novelty Detection as an Intrinsic Reward for Robotics". Sensors 23, nr 8 (14.04.2023): 3985. http://dx.doi.org/10.3390/s23083985.
Pełny tekst źródłaCatacora Ocana, Jim Martin, Roberto Capobianco i Daniele Nardi. "An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains". Journal of Artificial Intelligence Research 76 (26.04.2023): 1181–218. http://dx.doi.org/10.1613/jair.1.14390.
Pełny tekst źródłaZhou, Xiao, Song Zhou, Xingang Mou i Yi He. "Multirobot Collaborative Pursuit Target Robot by Improved MADDPG". Computational Intelligence and Neuroscience 2022 (25.02.2022): 1–10. http://dx.doi.org/10.1155/2022/4757394.
Pełny tekst źródłaVelasquez, Alvaro, Brett Bissey, Lior Barak, Andre Beckus, Ismail Alkhouri, Daniel Melcer i George Atia. "Dynamic Automaton-Guided Reward Shaping for Monte Carlo Tree Search". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 13 (18.05.2021): 12015–23. http://dx.doi.org/10.1609/aaai.v35i13.17427.
Pełny tekst źródłaYan Kong, Yan Kong, Yefeng Rui Yan Kong i Chih-Hsien Hsia Yefeng Rui. "A Deep Reinforcement Learning-Based Approach in Porker Game". 電腦學刊 34, nr 2 (kwiecień 2023): 041–51. http://dx.doi.org/10.53106/199115992023043402004.
Pełny tekst źródłaBougie, Nicolas, i Ryutaro Ichise. "Skill-based curiosity for intrinsically motivated reinforcement learning". Machine Learning 109, nr 3 (10.10.2019): 493–512. http://dx.doi.org/10.1007/s10994-019-05845-8.
Pełny tekst źródłaJiang, Jiechuan, i Zongqing Lu. "Generative Exploration and Exploitation". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 04 (3.04.2020): 4337–44. http://dx.doi.org/10.1609/aaai.v34i04.5858.
Pełny tekst źródłaHUANG, XIAO, i JUYANG WENG. "INHERENT VALUE SYSTEMS FOR AUTONOMOUS MENTAL DEVELOPMENT". International Journal of Humanoid Robotics 04, nr 02 (czerwiec 2007): 407–33. http://dx.doi.org/10.1142/s0219843607001011.
Pełny tekst źródłaLi, Yuangang, Tao Guo, Qinghua Li i Xinyue Liu. "Optimized Feature Extraction for Sample Efficient Deep Reinforcement Learning". Electronics 12, nr 16 (18.08.2023): 3508. http://dx.doi.org/10.3390/electronics12163508.
Pełny tekst źródłaTang, Wanxing, Chuang Cheng, Haiping Ai i Li Chen. "Dual-Arm Robot Trajectory Planning Based on Deep Reinforcement Learning under Complex Environment". Micromachines 13, nr 4 (31.03.2022): 564. http://dx.doi.org/10.3390/mi13040564.
Pełny tekst źródłaShah, Naman, i Siddharth Srivastava. "Hierarchical Planning and Learning for Robots in Stochastic Settings Using Zero-Shot Option Invention". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 9 (24.03.2024): 10358–67. http://dx.doi.org/10.1609/aaai.v38i9.28903.
Pełny tekst źródłaLi, Huale, Rui Cao, Xuan Wang, Xiaohan Hou, Tao Qian, Fengwei Jia, Jiajia Zhang i Shuhan Qi. "AIBPO: Combine the Intrinsic Reward and Auxiliary Task for 3D Strategy Game". Complexity 2021 (13.07.2021): 1–9. http://dx.doi.org/10.1155/2021/6698231.
Pełny tekst źródłaDharmavaram, Akshay, Matthew Riemer i Shalabh Bhatnagar. "Hierarchical Average Reward Policy Gradient Algorithms (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 10 (3.04.2020): 13777–78. http://dx.doi.org/10.1609/aaai.v34i10.7160.
Pełny tekst źródłaZhu, Chenyang, Yujie Cai, Jinyu Zhu, Can Hu i Jia Bi. "GR(1)-Guided Deep Reinforcement Learning for Multi-Task Motion Planning under a Stochastic Environment". Electronics 11, nr 22 (13.11.2022): 3716. http://dx.doi.org/10.3390/electronics11223716.
Pełny tekst źródłaRamakrishnan, Santhosh K., Dinesh Jayaraman i Kristen Grauman. "Emergence of exploratory look-around behaviors through active observation completion". Science Robotics 4, nr 30 (15.05.2019): eaaw6326. http://dx.doi.org/10.1126/scirobotics.aaw6326.
Pełny tekst źródłaHasanbeig, Mohammadhosein, Natasha Yogananda Jeppu, Alessandro Abate, Tom Melham i Daniel Kroening. "DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 9 (18.05.2021): 7647–56. http://dx.doi.org/10.1609/aaai.v35i9.16935.
Pełny tekst źródłaHan, Huiyan, Jiaqi Wang, Liqun Kuang, Xie Han i Hongxin Xue. "Improved Robot Path Planning Method Based on Deep Reinforcement Learning". Sensors 23, nr 12 (15.06.2023): 5622. http://dx.doi.org/10.3390/s23125622.
Pełny tekst źródłaZhang, Tengteng, i Hongwei Mo. "Research on Perception and Control Technology for Dexterous Robot Operation". Electronics 12, nr 14 (13.07.2023): 3065. http://dx.doi.org/10.3390/electronics12143065.
Pełny tekst źródłaNeider, Daniel, Jean-Raphael Gaglione, Ivan Gavran, Ufuk Topcu, Bo Wu i Zhe Xu. "Advice-Guided Reinforcement Learning in a non-Markovian Environment". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 10 (18.05.2021): 9073–80. http://dx.doi.org/10.1609/aaai.v35i10.17096.
Pełny tekst źródłaZhang, Xiaoping, Yihao Liu, Li Wang, Dunli Hu i Lei Liu. "A Curiosity-Based Autonomous Navigation Algorithm for Maze Robot". Journal of Advanced Computational Intelligence and Intelligent Informatics 26, nr 6 (20.11.2022): 893–904. http://dx.doi.org/10.20965/jaciii.2022.p0893.
Pełny tekst źródłaHan, Ziyao, Fan Yi i Kazuhiro Ohkura. "Collective Transport Behavior in a Robotic Swarm with Hierarchical Imitation Learning". Journal of Robotics and Mechatronics 36, nr 3 (20.06.2024): 538–45. http://dx.doi.org/10.20965/jrm.2024.p0538.
Pełny tekst źródłaAbu Bakar, Mohamad Hafiz, Abu Ubaidah Shamsudin, Zubair Adil Soomro, Satoshi Tadokoro i C. J. Salaan. "FUSION SPARSE AND SHAPING REWARD FUNCTION IN SOFT ACTOR-CRITIC DEEP REINFORCEMENT LEARNING FOR MOBILE ROBOT NAVIGATION". Jurnal Teknologi 86, nr 2 (15.01.2024): 37–49. http://dx.doi.org/10.11113/jurnalteknologi.v86.20147.
Pełny tekst źródłaSharip, Zati, Mohd Hafiz Zulkifli, Mohd Nur Farhan Abd Wahab, Zubaidi Johar i Mohd Zaki Mat Amin. "ASSESSING TROPHIC STATE AND WATER QUALITY OF SMALL LAKES AND PONDS IN PERAK". Jurnal Teknologi 86, nr 2 (15.01.2024): 51–59. http://dx.doi.org/10.11113/jurnalteknologi.v86.20566.
Pełny tekst źródłaSu, Linfeng, Jinbo Wang i Hongbo Chen. "A Real-Time and Optimal Hypersonic Entry Guidance Method Using Inverse Reinforcement Learning". Aerospace 10, nr 11 (7.11.2023): 948. http://dx.doi.org/10.3390/aerospace10110948.
Pełny tekst źródłaWang, Yifan, i Meibao Yao. "Autonomous Robots Traverse Multi-Terrain Environments via Hierarchical Reinforcement Learning with Skill Discovery". Journal of Physics: Conference Series 2762, nr 1 (1.05.2024): 012003. http://dx.doi.org/10.1088/1742-6596/2762/1/012003.
Pełny tekst źródłaZhang, Yilin, Huimin Sun, Honglin Sun, Yuan Huang i Kenji Hashimoto. "Biped Robots Control in Gusty Environments with Adaptive Exploration Based DDPG". Biomimetics 9, nr 6 (8.06.2024): 346. http://dx.doi.org/10.3390/biomimetics9060346.
Pełny tekst źródłaSong, Qingpeng, Yuansheng Liu, Ming Lu, Jun Zhang, Han Qi, Ziyu Wang i Zijian Liu. "Autonomous Driving Decision Control Based on Improved Proximal Policy Optimization Algorithm". Applied Sciences 13, nr 11 (24.05.2023): 6400. http://dx.doi.org/10.3390/app13116400.
Pełny tekst źródłaKim, MyeongSeop, i Jung-Su Kim. "Policy-based Deep Reinforcement Learning for Sparse Reward Environment". Transactions of The Korean Institute of Electrical Engineers 70, nr 3 (31.03.2021): 506–14. http://dx.doi.org/10.5370/kiee.2021.70.3.506.
Pełny tekst źródłaPotjans, Wiebke, Abigail Morrison i Markus Diesmann. "A Spiking Neural Network Model of an Actor-Critic Learning Agent". Neural Computation 21, nr 2 (luty 2009): 301–39. http://dx.doi.org/10.1162/neco.2008.08-07-593.
Pełny tekst źródłaRauber, Paulo, Avinash Ummadisingu, Filipe Mutz i Jürgen Schmidhuber. "Reinforcement Learning in Sparse-Reward Environments With Hindsight Policy Gradients". Neural Computation 33, nr 6 (13.05.2021): 1498–553. http://dx.doi.org/10.1162/neco_a_01387.
Pełny tekst źródłaYu, Sheng, Wei Zhu i Yong Wang. "Research on Wargame Decision-Making Method Based on Multi-Agent Deep Deterministic Policy Gradient". Applied Sciences 13, nr 7 (4.04.2023): 4569. http://dx.doi.org/10.3390/app13074569.
Pełny tekst źródłaZhang, Danyang, Zhaolong Xuan, Yang Zhang, Jiangyi Yao, Xi Li i Xiongwei Li. "Path Planning of Unmanned Aerial Vehicle in Complex Environments Based on State-Detection Twin Delayed Deep Deterministic Policy Gradient". Machines 11, nr 1 (13.01.2023): 108. http://dx.doi.org/10.3390/machines11010108.
Pełny tekst źródłaYao, Jiangyi, Xiongwei Li, Yang Zhang, Jingyu Ji, Yanchao Wang i Yicen Liu. "Path Planning of Unmanned Helicopter in Complex Dynamic Environment Based on State-Coded Deep Q-Network". Symmetry 14, nr 5 (21.04.2022): 856. http://dx.doi.org/10.3390/sym14050856.
Pełny tekst źródłaLei, Xiaoyun, Zhian Zhang i Peifang Dong. "Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning". Journal of Robotics 2018 (18.09.2018): 1–10. http://dx.doi.org/10.1155/2018/5781591.
Pełny tekst źródłaZhang, Zhizhuo, i Change Zheng. "Simulation of Robotic Arm Grasping Control Based on Proximal Policy Optimization Algorithm". Journal of Physics: Conference Series 2203, nr 1 (1.02.2022): 012065. http://dx.doi.org/10.1088/1742-6596/2203/1/012065.
Pełny tekst źródłaLuu, Tung M., i Chang D. Yoo. "Hindsight Goal Ranking on Replay Buffer for Sparse Reward Environment". IEEE Access 9 (2021): 51996–2007. http://dx.doi.org/10.1109/access.2021.3069975.
Pełny tekst źródłaFeng, Shiying, Xiaofeng Li, Lu Ren i Shuiqing Xu. "Reinforcement learning with parameterized action space and sparse reward for UAV navigation". Intelligence & Robotics 3, nr 2 (27.06.2023): 161–75. http://dx.doi.org/10.20517/ir.2023.10.
Pełny tekst źródłaLiu, Zeyang, Lipeng Wan, Xinrui Yang, Zhuoran Chen, Xingyu Chen i Xuguang Lan. "Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 16 (24.03.2024): 17487–95. http://dx.doi.org/10.1609/aaai.v38i16.29698.
Pełny tekst źródłaJiang, Haobin, Ziluo Ding i Zongqing Lu. "Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 16 (24.03.2024): 17444–52. http://dx.doi.org/10.1609/aaai.v38i16.29693.
Pełny tekst źródłaXu, He A., Alireza Modirshanechi, Marco P. Lehmann, Wulfram Gerstner i Michael H. Herzog. "Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making". PLOS Computational Biology 17, nr 6 (3.06.2021): e1009070. http://dx.doi.org/10.1371/journal.pcbi.1009070.
Pełny tekst źródłaZeng, Junjie, Rusheng Ju, Long Qin, Yue Hu, Quanjun Yin i Cong Hu. "Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning". Sensors 19, nr 18 (5.09.2019): 3837. http://dx.doi.org/10.3390/s19183837.
Pełny tekst źródłaPark, Minjae, Chaneun Park i Nam Kyu Kwon. "Autonomous Driving of Mobile Robots in Dynamic Environments Based on Deep Deterministic Policy Gradient: Reward Shaping and Hindsight Experience Replay". Biomimetics 9, nr 1 (13.01.2024): 51. http://dx.doi.org/10.3390/biomimetics9010051.
Pełny tekst źródłaMourad, Nafee, Ali Ezzeddine, Babak Nadjar Araabi i Majid Nili Ahmadabadi. "Learning from Demonstrations and Human Evaluative Feedbacks: Handling Sparsity and Imperfection Using Inverse Reinforcement Learning Approach". Journal of Robotics 2020 (13.01.2020): 1–18. http://dx.doi.org/10.1155/2020/3849309.
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