Artículos de revistas sobre el tema "Sparsely rewarded environments"
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Dubey, Rachit, Thomas L. Griffiths y Peter Dayan. "The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons". PLOS Computational Biology 18, n.º 8 (4 de agosto de 2022): e1010316. http://dx.doi.org/10.1371/journal.pcbi.1010316.
Texto completoShi, Xiaoping, Shiqi Zou, Shenmin Song y 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, n.º 5 (22 de abril de 2021): 10043–61. http://dx.doi.org/10.3233/jifs-202679.
Texto completoSakamoto, Yuma y Kentarou Kurashige. "Self-Generating Evaluations for Robot’s Autonomy Based on Sensor Input". Machines 11, n.º 9 (6 de septiembre de 2023): 892. http://dx.doi.org/10.3390/machines11090892.
Texto completoParisi, Simone, Davide Tateo, Maximilian Hensel, Carlo D’Eramo, Jan Peters y Joni Pajarinen. "Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning". Algorithms 15, n.º 3 (28 de febrero de 2022): 81. http://dx.doi.org/10.3390/a15030081.
Texto completoMguni, David, Taher Jafferjee, Jianhong Wang, Nicolas Perez-Nieves, Wenbin Song, Feifei Tong, Matthew Taylor et al. "Learning to Shape Rewards Using a Game of Two Partners". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 10 (26 de junio de 2023): 11604–12. http://dx.doi.org/10.1609/aaai.v37i10.26371.
Texto completoForbes, Grant C. y David L. Roberts. "Potential-Based Reward Shaping for Intrinsic Motivation (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 21 (24 de marzo de 2024): 23488–89. http://dx.doi.org/10.1609/aaai.v38i21.30441.
Texto completoXu, Pei, Junge Zhang, Qiyue Yin, Chao Yu, Yaodong Yang y Kaiqi Huang. "Subspace-Aware Exploration for Sparse-Reward Multi-Agent Tasks". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 10 (26 de junio de 2023): 11717–25. http://dx.doi.org/10.1609/aaai.v37i10.26384.
Texto completoKubovčík, Martin, Iveta Dirgová Luptáková y Jiří Pospíchal. "Signal Novelty Detection as an Intrinsic Reward for Robotics". Sensors 23, n.º 8 (14 de abril de 2023): 3985. http://dx.doi.org/10.3390/s23083985.
Texto completoCatacora Ocana, Jim Martin, Roberto Capobianco y Daniele Nardi. "An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains". Journal of Artificial Intelligence Research 76 (26 de abril de 2023): 1181–218. http://dx.doi.org/10.1613/jair.1.14390.
Texto completoZhou, Xiao, Song Zhou, Xingang Mou y Yi He. "Multirobot Collaborative Pursuit Target Robot by Improved MADDPG". Computational Intelligence and Neuroscience 2022 (25 de febrero de 2022): 1–10. http://dx.doi.org/10.1155/2022/4757394.
Texto completoVelasquez, Alvaro, Brett Bissey, Lior Barak, Andre Beckus, Ismail Alkhouri, Daniel Melcer y George Atia. "Dynamic Automaton-Guided Reward Shaping for Monte Carlo Tree Search". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de mayo de 2021): 12015–23. http://dx.doi.org/10.1609/aaai.v35i13.17427.
Texto completoYan Kong, Yan Kong, Yefeng Rui Yan Kong y Chih-Hsien Hsia Yefeng Rui. "A Deep Reinforcement Learning-Based Approach in Porker Game". 電腦學刊 34, n.º 2 (abril de 2023): 041–51. http://dx.doi.org/10.53106/199115992023043402004.
Texto completoBougie, Nicolas y Ryutaro Ichise. "Skill-based curiosity for intrinsically motivated reinforcement learning". Machine Learning 109, n.º 3 (10 de octubre de 2019): 493–512. http://dx.doi.org/10.1007/s10994-019-05845-8.
Texto completoJiang, Jiechuan y Zongqing Lu. "Generative Exploration and Exploitation". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 4337–44. http://dx.doi.org/10.1609/aaai.v34i04.5858.
Texto completoHUANG, XIAO y JUYANG WENG. "INHERENT VALUE SYSTEMS FOR AUTONOMOUS MENTAL DEVELOPMENT". International Journal of Humanoid Robotics 04, n.º 02 (junio de 2007): 407–33. http://dx.doi.org/10.1142/s0219843607001011.
Texto completoLi, Yuangang, Tao Guo, Qinghua Li y Xinyue Liu. "Optimized Feature Extraction for Sample Efficient Deep Reinforcement Learning". Electronics 12, n.º 16 (18 de agosto de 2023): 3508. http://dx.doi.org/10.3390/electronics12163508.
Texto completoTang, Wanxing, Chuang Cheng, Haiping Ai y Li Chen. "Dual-Arm Robot Trajectory Planning Based on Deep Reinforcement Learning under Complex Environment". Micromachines 13, n.º 4 (31 de marzo de 2022): 564. http://dx.doi.org/10.3390/mi13040564.
Texto completoShah, Naman y 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, n.º 9 (24 de marzo de 2024): 10358–67. http://dx.doi.org/10.1609/aaai.v38i9.28903.
Texto completoLi, Huale, Rui Cao, Xuan Wang, Xiaohan Hou, Tao Qian, Fengwei Jia, Jiajia Zhang y Shuhan Qi. "AIBPO: Combine the Intrinsic Reward and Auxiliary Task for 3D Strategy Game". Complexity 2021 (13 de julio de 2021): 1–9. http://dx.doi.org/10.1155/2021/6698231.
Texto completoDharmavaram, Akshay, Matthew Riemer y Shalabh Bhatnagar. "Hierarchical Average Reward Policy Gradient Algorithms (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 10 (3 de abril de 2020): 13777–78. http://dx.doi.org/10.1609/aaai.v34i10.7160.
Texto completoZhu, Chenyang, Yujie Cai, Jinyu Zhu, Can Hu y Jia Bi. "GR(1)-Guided Deep Reinforcement Learning for Multi-Task Motion Planning under a Stochastic Environment". Electronics 11, n.º 22 (13 de noviembre de 2022): 3716. http://dx.doi.org/10.3390/electronics11223716.
Texto completoRamakrishnan, Santhosh K., Dinesh Jayaraman y Kristen Grauman. "Emergence of exploratory look-around behaviors through active observation completion". Science Robotics 4, n.º 30 (15 de mayo de 2019): eaaw6326. http://dx.doi.org/10.1126/scirobotics.aaw6326.
Texto completoHasanbeig, Mohammadhosein, Natasha Yogananda Jeppu, Alessandro Abate, Tom Melham y Daniel Kroening. "DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 9 (18 de mayo de 2021): 7647–56. http://dx.doi.org/10.1609/aaai.v35i9.16935.
Texto completoHan, Huiyan, Jiaqi Wang, Liqun Kuang, Xie Han y Hongxin Xue. "Improved Robot Path Planning Method Based on Deep Reinforcement Learning". Sensors 23, n.º 12 (15 de junio de 2023): 5622. http://dx.doi.org/10.3390/s23125622.
Texto completoZhang, Tengteng y Hongwei Mo. "Research on Perception and Control Technology for Dexterous Robot Operation". Electronics 12, n.º 14 (13 de julio de 2023): 3065. http://dx.doi.org/10.3390/electronics12143065.
Texto completoNeider, Daniel, Jean-Raphael Gaglione, Ivan Gavran, Ufuk Topcu, Bo Wu y Zhe Xu. "Advice-Guided Reinforcement Learning in a non-Markovian Environment". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 10 (18 de mayo de 2021): 9073–80. http://dx.doi.org/10.1609/aaai.v35i10.17096.
Texto completoZhang, Xiaoping, Yihao Liu, Li Wang, Dunli Hu y Lei Liu. "A Curiosity-Based Autonomous Navigation Algorithm for Maze Robot". Journal of Advanced Computational Intelligence and Intelligent Informatics 26, n.º 6 (20 de noviembre de 2022): 893–904. http://dx.doi.org/10.20965/jaciii.2022.p0893.
Texto completoHan, Ziyao, Fan Yi y Kazuhiro Ohkura. "Collective Transport Behavior in a Robotic Swarm with Hierarchical Imitation Learning". Journal of Robotics and Mechatronics 36, n.º 3 (20 de junio de 2024): 538–45. http://dx.doi.org/10.20965/jrm.2024.p0538.
Texto completoAbu Bakar, Mohamad Hafiz, Abu Ubaidah Shamsudin, Zubair Adil Soomro, Satoshi Tadokoro y C. J. Salaan. "FUSION SPARSE AND SHAPING REWARD FUNCTION IN SOFT ACTOR-CRITIC DEEP REINFORCEMENT LEARNING FOR MOBILE ROBOT NAVIGATION". Jurnal Teknologi 86, n.º 2 (15 de enero de 2024): 37–49. http://dx.doi.org/10.11113/jurnalteknologi.v86.20147.
Texto completoSharip, Zati, Mohd Hafiz Zulkifli, Mohd Nur Farhan Abd Wahab, Zubaidi Johar y Mohd Zaki Mat Amin. "ASSESSING TROPHIC STATE AND WATER QUALITY OF SMALL LAKES AND PONDS IN PERAK". Jurnal Teknologi 86, n.º 2 (15 de enero de 2024): 51–59. http://dx.doi.org/10.11113/jurnalteknologi.v86.20566.
Texto completoSu, Linfeng, Jinbo Wang y Hongbo Chen. "A Real-Time and Optimal Hypersonic Entry Guidance Method Using Inverse Reinforcement Learning". Aerospace 10, n.º 11 (7 de noviembre de 2023): 948. http://dx.doi.org/10.3390/aerospace10110948.
Texto completoWang, Yifan y Meibao Yao. "Autonomous Robots Traverse Multi-Terrain Environments via Hierarchical Reinforcement Learning with Skill Discovery". Journal of Physics: Conference Series 2762, n.º 1 (1 de mayo de 2024): 012003. http://dx.doi.org/10.1088/1742-6596/2762/1/012003.
Texto completoZhang, Yilin, Huimin Sun, Honglin Sun, Yuan Huang y Kenji Hashimoto. "Biped Robots Control in Gusty Environments with Adaptive Exploration Based DDPG". Biomimetics 9, n.º 6 (8 de junio de 2024): 346. http://dx.doi.org/10.3390/biomimetics9060346.
Texto completoSong, Qingpeng, Yuansheng Liu, Ming Lu, Jun Zhang, Han Qi, Ziyu Wang y Zijian Liu. "Autonomous Driving Decision Control Based on Improved Proximal Policy Optimization Algorithm". Applied Sciences 13, n.º 11 (24 de mayo de 2023): 6400. http://dx.doi.org/10.3390/app13116400.
Texto completoKim, MyeongSeop y Jung-Su Kim. "Policy-based Deep Reinforcement Learning for Sparse Reward Environment". Transactions of The Korean Institute of Electrical Engineers 70, n.º 3 (31 de marzo de 2021): 506–14. http://dx.doi.org/10.5370/kiee.2021.70.3.506.
Texto completoPotjans, Wiebke, Abigail Morrison y Markus Diesmann. "A Spiking Neural Network Model of an Actor-Critic Learning Agent". Neural Computation 21, n.º 2 (febrero de 2009): 301–39. http://dx.doi.org/10.1162/neco.2008.08-07-593.
Texto completoRauber, Paulo, Avinash Ummadisingu, Filipe Mutz y Jürgen Schmidhuber. "Reinforcement Learning in Sparse-Reward Environments With Hindsight Policy Gradients". Neural Computation 33, n.º 6 (13 de mayo de 2021): 1498–553. http://dx.doi.org/10.1162/neco_a_01387.
Texto completoYu, Sheng, Wei Zhu y Yong Wang. "Research on Wargame Decision-Making Method Based on Multi-Agent Deep Deterministic Policy Gradient". Applied Sciences 13, n.º 7 (4 de abril de 2023): 4569. http://dx.doi.org/10.3390/app13074569.
Texto completoZhang, Danyang, Zhaolong Xuan, Yang Zhang, Jiangyi Yao, Xi Li y Xiongwei Li. "Path Planning of Unmanned Aerial Vehicle in Complex Environments Based on State-Detection Twin Delayed Deep Deterministic Policy Gradient". Machines 11, n.º 1 (13 de enero de 2023): 108. http://dx.doi.org/10.3390/machines11010108.
Texto completoYao, Jiangyi, Xiongwei Li, Yang Zhang, Jingyu Ji, Yanchao Wang y Yicen Liu. "Path Planning of Unmanned Helicopter in Complex Dynamic Environment Based on State-Coded Deep Q-Network". Symmetry 14, n.º 5 (21 de abril de 2022): 856. http://dx.doi.org/10.3390/sym14050856.
Texto completoLei, Xiaoyun, Zhian Zhang y Peifang Dong. "Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning". Journal of Robotics 2018 (18 de septiembre de 2018): 1–10. http://dx.doi.org/10.1155/2018/5781591.
Texto completoZhang, Zhizhuo y Change Zheng. "Simulation of Robotic Arm Grasping Control Based on Proximal Policy Optimization Algorithm". Journal of Physics: Conference Series 2203, n.º 1 (1 de febrero de 2022): 012065. http://dx.doi.org/10.1088/1742-6596/2203/1/012065.
Texto completoLuu, Tung M. y 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.
Texto completoFeng, Shiying, Xiaofeng Li, Lu Ren y Shuiqing Xu. "Reinforcement learning with parameterized action space and sparse reward for UAV navigation". Intelligence & Robotics 3, n.º 2 (27 de junio de 2023): 161–75. http://dx.doi.org/10.20517/ir.2023.10.
Texto completoLiu, Zeyang, Lipeng Wan, Xinrui Yang, Zhuoran Chen, Xingyu Chen y Xuguang Lan. "Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 16 (24 de marzo de 2024): 17487–95. http://dx.doi.org/10.1609/aaai.v38i16.29698.
Texto completoJiang, Haobin, Ziluo Ding y Zongqing Lu. "Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 16 (24 de marzo de 2024): 17444–52. http://dx.doi.org/10.1609/aaai.v38i16.29693.
Texto completoXu, He A., Alireza Modirshanechi, Marco P. Lehmann, Wulfram Gerstner y Michael H. Herzog. "Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making". PLOS Computational Biology 17, n.º 6 (3 de junio de 2021): e1009070. http://dx.doi.org/10.1371/journal.pcbi.1009070.
Texto completoZeng, Junjie, Rusheng Ju, Long Qin, Yue Hu, Quanjun Yin y Cong Hu. "Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning". Sensors 19, n.º 18 (5 de septiembre de 2019): 3837. http://dx.doi.org/10.3390/s19183837.
Texto completoPark, Minjae, Chaneun Park y 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, n.º 1 (13 de enero de 2024): 51. http://dx.doi.org/10.3390/biomimetics9010051.
Texto completoMourad, Nafee, Ali Ezzeddine, Babak Nadjar Araabi y Majid Nili Ahmadabadi. "Learning from Demonstrations and Human Evaluative Feedbacks: Handling Sparsity and Imperfection Using Inverse Reinforcement Learning Approach". Journal of Robotics 2020 (13 de enero de 2020): 1–18. http://dx.doi.org/10.1155/2020/3849309.
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