Journal articles on the topic 'Reinforcement Learning Generalization'
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Kwon, Sunggyu, and Kwang Y. Lee. "GENERALIZATION OF REINFORCEMENT LEARNING WITH CMAC." IFAC Proceedings Volumes 38, no. 1 (2005): 360–65. http://dx.doi.org/10.3182/20050703-6-cz-1902.01138.
Full textWu, Keyu, Min Wu, Zhenghua Chen, Yuecong Xu, and Xiaoli Li. "Generalizing Reinforcement Learning through Fusing Self-Supervised Learning into Intrinsic Motivation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8683–90. http://dx.doi.org/10.1609/aaai.v36i8.20847.
Full textWimmer, G. Elliott, Nathaniel D. Daw, and Daphna Shohamy. "Generalization of value in reinforcement learning by humans." European Journal of Neuroscience 35, no. 7 (April 2012): 1092–104. http://dx.doi.org/10.1111/j.1460-9568.2012.08017.x.
Full textHashemzadeh, Maryam, Reshad Hosseini, and Majid Nili Ahmadabadi. "Clustering subspace generalization to obtain faster reinforcement learning." Evolving Systems 11, no. 1 (July 4, 2019): 89–103. http://dx.doi.org/10.1007/s12530-019-09290-9.
Full textGershman, Samuel J., and Yael Niv. "Novelty and Inductive Generalization in Human Reinforcement Learning." Topics in Cognitive Science 7, no. 3 (March 23, 2015): 391–415. http://dx.doi.org/10.1111/tops.12138.
Full textMatiisen, Tambet, Aqeel Labash, Daniel Majoral, Jaan Aru, and Raul Vicente. "Do Deep Reinforcement Learning Agents Model Intentions?" Stats 6, no. 1 (December 28, 2022): 50–66. http://dx.doi.org/10.3390/stats6010004.
Full textFang, Qiang, Wenzhuo Zhang, and Xitong Wang. "Visual Navigation Using Inverse Reinforcement Learning and an Extreme Learning Machine." Electronics 10, no. 16 (August 18, 2021): 1997. http://dx.doi.org/10.3390/electronics10161997.
Full textHatcho, Yasuyo, Kiyohiko Hattori, and Keiki Takadama. "Time Horizon Generalization in Reinforcement Learning: Generalizing Multiple Q-Tables in Q-Learning Agents." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 6 (November 20, 2009): 667–74. http://dx.doi.org/10.20965/jaciii.2009.p0667.
Full textKaelbling, L. P., M. L. Littman, and A. W. Moore. "Reinforcement Learning: A Survey." Journal of Artificial Intelligence Research 4 (May 1, 1996): 237–85. http://dx.doi.org/10.1613/jair.301.
Full textKim, Minbeom, Kyeongha Rho, Yong-duk Kim, and Kyomin Jung. "Action-driven contrastive representation for reinforcement learning." PLOS ONE 17, no. 3 (March 18, 2022): e0265456. http://dx.doi.org/10.1371/journal.pone.0265456.
Full textMatsushima, Hiroyasu, Kiyohiko Hattori, and Keiki Takadama. "Exemplar Generalization in Reinforcement Learning: Improving Performance with Fewer Exemplars." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 6 (November 20, 2009): 683–90. http://dx.doi.org/10.20965/jaciii.2009.p0683.
Full textWang, Cong, Qifeng Zhang, Qiyan Tian, Shuo Li, Xiaohui Wang, David Lane, Yvan Petillot, and Sen Wang. "Learning Mobile Manipulation through Deep Reinforcement Learning." Sensors 20, no. 3 (February 10, 2020): 939. http://dx.doi.org/10.3390/s20030939.
Full textWilliams, Arthur, and Joshua Phillips. "Transfer Reinforcement Learning Using Output-Gated Working Memory." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1324–31. http://dx.doi.org/10.1609/aaai.v34i02.5488.
Full textBotteghi, N., B. Sirmacek, R. Schulte, M. Poel, and C. Brune. "REINFORCEMENT LEARNING HELPS SLAM: LEARNING TO BUILD MAPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 329–35. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-329-2020.
Full textFrancois-Lavet, Vincent, Yoshua Bengio, Doina Precup, and Joelle Pineau. "Combined Reinforcement Learning via Abstract Representations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3582–89. http://dx.doi.org/10.1609/aaai.v33i01.33013582.
Full textGraham, Robert B. "A Computer Tutorial on the Principles of Stimulus Generalization." Teaching of Psychology 25, no. 2 (April 1998): 149–51. http://dx.doi.org/10.1207/s15328023top2502_21.
Full textTamar, Aviv, Daniel Soudry, and Ev Zisselman. "Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8423–31. http://dx.doi.org/10.1609/aaai.v36i8.20818.
Full textVickery, Timothy, and Kyle Friedman. "Generalization of value to visual statistical associates during reinforcement learning." Journal of Vision 15, no. 12 (September 1, 2015): 1350. http://dx.doi.org/10.1167/15.12.1350.
Full textWen, Zheng, and Benjamin Van Roy. "Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization." Mathematics of Operations Research 42, no. 3 (August 2017): 762–82. http://dx.doi.org/10.1287/moor.2016.0826.
Full textHirashima, Yoichi. "A New Reinforcement Learning for Train Marshaling with Generalization Capability." Advanced Materials Research 974 (June 2014): 269–73. http://dx.doi.org/10.4028/www.scientific.net/amr.974.269.
Full textGoto, Ryo, and Hiroshi Matsuo. "State generalization method with support vector machines in reinforcement learning." Systems and Computers in Japan 37, no. 9 (2006): 77–86. http://dx.doi.org/10.1002/scj.20140.
Full textPerrin, Sarah, Mathieu Laurière, Julien Pérolat, Romuald Élie, Matthieu Geist, and Olivier Pietquin. "Generalization in Mean Field Games by Learning Master Policies." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 9413–21. http://dx.doi.org/10.1609/aaai.v36i9.21173.
Full textGershman, Samuel J., Christopher D. Moore, Michael T. Todd, Kenneth A. Norman, and Per B. Sederberg. "The Successor Representation and Temporal Context." Neural Computation 24, no. 6 (June 2012): 1553–68. http://dx.doi.org/10.1162/neco_a_00282.
Full textWu, Haiping, Khimya Khetarpal, and Doina Precup. "Self-Supervised Attention-Aware Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10311–19. http://dx.doi.org/10.1609/aaai.v35i12.17235.
Full textGao, Junli, Weijie Ye, Jing Guo, and Zhongjuan Li. "Deep Reinforcement Learning for Indoor Mobile Robot Path Planning." Sensors 20, no. 19 (September 25, 2020): 5493. http://dx.doi.org/10.3390/s20195493.
Full textGustafson, Nicholas J., and Nathaniel D. Daw. "Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning." PLoS Computational Biology 7, no. 10 (October 27, 2011): e1002235. http://dx.doi.org/10.1371/journal.pcbi.1002235.
Full textFonteneau, R., D. Ernst, B. Boigelot, and Q. Louveaux. "Min Max Generalization for Deterministic Batch Mode Reinforcement Learning: Relaxation Schemes." SIAM Journal on Control and Optimization 51, no. 5 (January 2013): 3355–85. http://dx.doi.org/10.1137/120867263.
Full textHashemzadeh, Maryam, Reshad Hosseini, and Majid Nili Ahmadabadi. "Exploiting Generalization in the Subspaces for Faster Model-Based Reinforcement Learning." IEEE Transactions on Neural Networks and Learning Systems 30, no. 6 (June 2019): 1635–50. http://dx.doi.org/10.1109/tnnls.2018.2869978.
Full textCilden, Erkin, and Faruk Polat. "Toward Generalization of Automated Temporal Abstraction to Partially Observable Reinforcement Learning." IEEE Transactions on Cybernetics 45, no. 8 (August 2015): 1414–25. http://dx.doi.org/10.1109/tcyb.2014.2352038.
Full textHu and Xu. "Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation." Information 10, no. 11 (November 2, 2019): 341. http://dx.doi.org/10.3390/info10110341.
Full textZhang, Yichuan, Yixing Lan, Qiang Fang, Xin Xu, Junxiang Li, and Yujun Zeng. "Efficient Reinforcement Learning from Demonstration via Bayesian Network-Based Knowledge Extraction." Computational Intelligence and Neuroscience 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/7588221.
Full textZhou, Li, and Kevin Small. "Inverse Reinforcement Learning with Natural Language Goals." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 11116–24. http://dx.doi.org/10.1609/aaai.v35i12.17326.
Full textUgurlu, Halil Ibrahim, Xuan Huy Pham, and Erdal Kayacan. "Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots." Robotics 11, no. 5 (October 13, 2022): 109. http://dx.doi.org/10.3390/robotics11050109.
Full textLandry, Jean-Francois, J. J. McArthur, Mikhail Genkin, and Karim El Mokhtari. "Development of the Reward Function to support Model-Free Reinforcement Learning for a Heat Recovery Chiller System Optimization." IOP Conference Series: Earth and Environmental Science 1101, no. 9 (November 1, 2022): 092027. http://dx.doi.org/10.1088/1755-1315/1101/9/092027.
Full textHerlau, Tue, and Rasmus Larsen. "Reinforcement Learning of Causal Variables Using Mediation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6910–17. http://dx.doi.org/10.1609/aaai.v36i6.20648.
Full textKim and Park. "Exploration with Multiple Random ε-Buffers in Off-Policy Deep Reinforcement Learning." Symmetry 11, no. 11 (November 1, 2019): 1352. http://dx.doi.org/10.3390/sym11111352.
Full textDevo, Alessandro, Giacomo Mezzetti, Gabriele Costante, Mario L. Fravolini, and Paolo Valigi. "Towards Generalization in Target-Driven Visual Navigation by Using Deep Reinforcement Learning." IEEE Transactions on Robotics 36, no. 5 (October 2020): 1546–61. http://dx.doi.org/10.1109/tro.2020.2994002.
Full textIima, Hitoshi, and Hiroya Oonishi. "Solution of an Optimal Routing Problem by Reinforcement Learning with Generalization Ability." IEEJ Transactions on Electronics, Information and Systems 139, no. 12 (December 1, 2019): 1494–500. http://dx.doi.org/10.1541/ieejeiss.139.1494.
Full textLiu, Rongrong, Florent Nageotte, Philippe Zanne, Michel de Mathelin, and Birgitta Dresp-Langley. "Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review." Robotics 10, no. 1 (January 24, 2021): 22. http://dx.doi.org/10.3390/robotics10010022.
Full textIkegami, Tsuyoshi, J. Randall Flanagan, and Daniel M. Wolpert. "Reach adaption to a visuomotor gain with terminal error feedback involves reinforcement learning." PLOS ONE 17, no. 6 (June 1, 2022): e0269297. http://dx.doi.org/10.1371/journal.pone.0269297.
Full textBarreto, André, Shaobo Hou, Diana Borsa, David Silver, and Doina Precup. "Fast reinforcement learning with generalized policy updates." Proceedings of the National Academy of Sciences 117, no. 48 (August 17, 2020): 30079–87. http://dx.doi.org/10.1073/pnas.1907370117.
Full textLou, Ping, Kun Xu, Xuemei Jiang, Zheng Xiao, and Junwei Yan. "Path planning in an unknown environment based on deep reinforcement learning with prior knowledge." Journal of Intelligent & Fuzzy Systems 41, no. 6 (December 16, 2021): 5773–89. http://dx.doi.org/10.3233/jifs-192171.
Full textGao, Xiaoyu, Shipin Yang, and Lijuan Li. "Optimization of flow shop scheduling based on genetic algorithm with reinforcement learning." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012019. http://dx.doi.org/10.1088/1742-6596/2258/1/012019.
Full textKeith, Kenneth D. "Peak Shift Phenomenon: A Teaching Activity for Basic Learning Theory." Teaching of Psychology 29, no. 4 (October 2002): 298–300. http://dx.doi.org/10.1207/s15328023top2904_09.
Full textGomolka, Zbigniew, Ewa Dudek-Dyduch, and Ewa Zeslawska. "Generalization of ALMM Based Learning Method for Planning and Scheduling." Applied Sciences 12, no. 24 (December 12, 2022): 12766. http://dx.doi.org/10.3390/app122412766.
Full textLi, Bo, Zhigang Gan, Daqing Chen, and Dyachenko Sergey Aleksandrovich. "UAV Maneuvering Target Tracking in Uncertain Environments Based on Deep Reinforcement Learning and Meta-Learning." Remote Sensing 12, no. 22 (November 18, 2020): 3789. http://dx.doi.org/10.3390/rs12223789.
Full textMurugesan, Keerthiram, Subhajit Chaudhury, and Kartik Talamadupula. "Eye of the Beholder: Improved Relation Generalization for Text-Based Reinforcement Learning Agents." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11094–102. http://dx.doi.org/10.1609/aaai.v36i10.21358.
Full textLi, Shang, Xin Chen, Min Zhang, Qingchen Jin, Yudi Guo, and Shunxiang Xing. "A UAV Coverage Path Planning Algorithm Based on Double Deep Q-Network." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012017. http://dx.doi.org/10.1088/1742-6596/2216/1/012017.
Full textKimura, Hajime, Kei Aoki, and Shigenobu Kobayashi. "Reinforcement Learning in Large Scale Systems Using State Generalization and Multi-Agent Techniques." IEEJ Transactions on Industry Applications 123, no. 10 (2003): 1091–96. http://dx.doi.org/10.1541/ieejias.123.1091.
Full textBarbahan, Ibraheem, Vladimir Baikalov, Valeriy Vyatkin, and Andrey Filchenkov. "Multi-Agent Deep Reinforcement Learning-Based Algorithm For Fast Generalization On Routing Problems." Procedia Computer Science 193 (2021): 228–38. http://dx.doi.org/10.1016/j.procs.2021.10.023.
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