Reinforcement learning (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Reinforcement learning" in English language version.

refsWebsite

Global rank English rank

33doi.org

2^nd place

23arxiv.org

69^th place

59^th place

20semanticscholar.org

11^th place

8^th place

10nih.gov

4^th place

7harvard.edu

18^th place

17^th place

7worldcat.org

5^th place

5ieee.org

652^nd place

515^th place

4incompleteideas.net

low place

4springer.com

274^th place

309^th place

4acm.org

1,185^th place

840^th place

3web.archive.org

1^st place

3neurips.cc

low place

2psu.edu

207^th place

136^th place

2mlr.press

low place

2openreview.net

low place

2umass.edu

3,341^st place

2,191^st place

1loc.gov

70^th place

63^rd place

1washington.edu

1,067^th place

749^th place

1jstor.org

26^th place

20^th place

1tokic.com

low place

1elte.hu

5,814^th place

7,639^th place

1rhul.ac.uk

low place

9,338^th place

1usc.edu

1,077^th place

761^st place

1medium.com

551^st place

406^th place

1lincoln.ac.uk

low place

1handle.net

102^nd place

76^th place

1sciencedirect.com

149^th place

178^th place

1clemenswinter.com

low place

1pbworks.com

7,275^th place

6,157^th place

1essex.ac.uk

9,925^th place

6,538^th place

1businessweekly.co.uk

low place

1inews.co.uk

1,084^th place

617^th place

1ssrn.com

703^rd place

501^st place

1uni-miskolc.hu

low place

1stanford.edu

179^th place

183^rd place

1jmlr.org

low place

1scholar.google.com

1,697^th place

1,040^th place

1aaai.org

9,352^nd place

5,696^th place

aaai.org

ojs.aaai.org

Tamar, Aviv; Glassner, Yonatan; Mannor, Shie (2015-02-21). "Optimizing the CVaR via Sampling". Proceedings of the AAAI Conference on Artificial Intelligence. 29 (1). arXiv:1404.3862. doi:10.1609/aaai.v29i1.9561. ISSN 2374-3468.

acm.org

dl.acm.org

Grondman, Ivo; Vaandrager, Maarten; Busoniu, Lucian; Babuska, Robert; Schuitema, Erik (2012-06-01). "Efficient Model Learning Methods for Actor–Critic Control". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 42 (3): 591–602. doi:10.1109/TSMCB.2011.2170565. ISSN 1083-4419. PMID 22156998.
Kulkarni, Tejas D.; Narasimhan, Karthik R.; Saeedi, Ardavan; Tenenbaum, Joshua B. (2016). "Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation". Proceedings of the 30th International Conference on Neural Information Processing Systems. NIPS'16. USA: Curran Associates Inc.: 3682–3690. arXiv:1604.06057. Bibcode:2016arXiv160406057K. ISBN 978-1-5108-3881-9.
Ziebart, Brian D.; Maas, Andrew; Bagnell, J. Andrew; Dey, Anind K. (2008-07-13). "Maximum entropy inverse reinforcement learning". Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 3. AAAI'08. Chicago, Illinois: AAAI Press: 1433–1438. ISBN 978-1-57735-368-3. S2CID 336219.

cie.acm.org

"On the Use of Reinforcement Learning for Testing Game Mechanics : ACM - Computers in Entertainment". cie.acm.org. Retrieved 2018-11-27.

arxiv.org

Kaelbling, Leslie P.; Littman, Michael L.; Moore, Andrew W. (1996). "Reinforcement Learning: A Survey". Journal of Artificial Intelligence Research. 4: 237–285. arXiv:cs/9605103. doi:10.1613/jair.301. S2CID 1708582. Archived from the original on 2001-11-20.
Xie, Zhaoming; Hung Yu Ling; Nam Hee Kim; Michiel van de Panne (2020). "ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills". arXiv:2005.04323 [cs.GR].
Ren, Yangang; Jiang, Jianhua; Zhan, Guojian; Li, Shengbo Eben; Chen, Chen; Li, Keqiang; Duan, Jingliang (2022). "Self-Learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections". IEEE Transactions on Intelligent Transportation Systems. 23 (12): 24145–24156. arXiv:2110.12359. doi:10.1109/TITS.2022.3196167.
Haramati, Dan; Daniel, Tal; Tamar, Aviv (2024). "Entity-Centric Reinforcement Learning for Object Manipulation from Pixels". arXiv:2404.01220 [cs.RO].
Thompson, Isaac Symes; Caron, Alberto; Hicks, Chris; Mavroudis, Vasilios (2024-11-07). "Entity-based Reinforcement Learning for Autonomous Cyber Defence". Proceedings of the Workshop on Autonomous Cybersecurity (AutonomousCyber '24). ACM. pp. 56–67. arXiv:2410.17647. doi:10.1145/3689933.3690835.
Yamagata, Taku; McConville, Ryan; Santos-Rodriguez, Raul (2021-11-16). "Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills". arXiv:2111.08596 [cs.LG].
Kulkarni, Tejas D.; Narasimhan, Karthik R.; Saeedi, Ardavan; Tenenbaum, Joshua B. (2016). "Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation". Proceedings of the 30th International Conference on Neural Information Processing Systems. NIPS'16. USA: Curran Associates Inc.: 3682–3690. arXiv:1604.06057. Bibcode:2016arXiv160406057K. ISBN 978-1-5108-3881-9.
George Karimpanal, Thommen; Bouffanais, Roland (2019). "Self-organizing maps for storage and transfer of knowledge in reinforcement learning". Adaptive Behavior. 27 (2): 111–126. arXiv:1811.08318. doi:10.1177/1059712318818568. ISSN 1059-7123. S2CID 53774629.
J Duan; Y Guan; S Li (2021). "Distributional Soft Actor-Critic: Off-policy reinforcement learning for addressing value estimation errors". IEEE Transactions on Neural Networks and Learning Systems. 33 (11): 6584–6598. arXiv:2001.02811. doi:10.1109/TNNLS.2021.3082568. PMID 34101599. S2CID 211259373.
Y Ren; J Duan; S Li (2020). "Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic". 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). pp. 1–6. arXiv:2002.05502. doi:10.1109/ITSC45102.2020.9294300. ISBN 978-1-7281-4149-7. S2CID 211096594.
Duan, J; Wang, W; Xiao, L (2025). "Distributional Soft Actor-Critic with Three Refinements". IEEE Transactions on Pattern Analysis and Machine Intelligence. PP (5): 3935–3946. arXiv:2310.05858. doi:10.1109/TPAMI.2025.3537087. PMID 40031258.
Francois-Lavet, Vincent; et al. (2018). "An Introduction to Deep Reinforcement Learning". Foundations and Trends in Machine Learning. 11 (3–4): 219–354. arXiv:1811.12560. Bibcode:2018arXiv181112560F. doi:10.1561/2200000071. S2CID 54434537.
Goodfellow, Ian; Shlens, Jonathan; Szegedy, Christian (2015). "Explaining and Harnessing Adversarial Examples". International Conference on Learning Representations. arXiv:1412.6572.
Behzadan, Vahid; Munir, Arslan (2017). "Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks". Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science. Vol. 10358. pp. 262–275. arXiv:1701.04143. doi:10.1007/978-3-319-62416-7_19. ISBN 978-3-319-62415-0. S2CID 1562290.
Korkmaz, Ezgi (2022). "Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs". Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). 36 (7): 7229–7238. arXiv:2112.09025. doi:10.1609/aaai.v36i7.20684. S2CID 245219157.
Pitombeira-Neto, Anselmo R.; Santos, Helano P.; Coelho da Silva, Ticiana L.; de Macedo, José Antonio F. (March 2024). "Trajectory modeling via random utility inverse reinforcement learning". Information Sciences. 660: 120128. arXiv:2105.12092. doi:10.1016/j.ins.2024.120128. ISSN 0020-0255. S2CID 235187141.
Hayes C, Radulescu R, Bargiacchi E, et al. (2022). "A practical guide to multi-objective reinforcement learning and planning". Autonomous Agents and Multi-Agent Systems. 36. arXiv:2103.09568. doi:10.1007/s10458-022-09552-y. S2CID 254235920.,
Dabney, Will; Ostrovski, Georg; Silver, David; Munos, Remi (2018-07-03). "Implicit Quantile Networks for Distributional Reinforcement Learning". Proceedings of the 35th International Conference on Machine Learning. PMLR: 1096–1105. arXiv:1806.06923.
Chow, Yinlam; Tamar, Aviv; Mannor, Shie; Pavone, Marco (2015). "Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach". Advances in Neural Information Processing Systems. 28. Curran Associates, Inc. arXiv:1506.02188.
Tamar, Aviv; Glassner, Yonatan; Mannor, Shie (2015-02-21). "Optimizing the CVaR via Sampling". Proceedings of the AAAI Conference on Artificial Intelligence. 29 (1). arXiv:1404.3862. doi:10.1609/aaai.v29i1.9561. ISSN 2374-3468.
Greenberg, Ido; Chow, Yinlam; Ghavamzadeh, Mohammad; Mannor, Shie (2022-12-06). "Efficient Risk-Averse Reinforcement Learning". Advances in Neural Information Processing Systems. 35: 32639–32652. arXiv:2205.05138.
Colas, Cédric (2019-03-06). "A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms". International Conference on Learning Representations. arXiv:1904.06979.
Greenberg, Ido; Mannor, Shie (2021-07-01). "Detecting Rewards Deterioration in Episodic Reinforcement Learning". Proceedings of the 38th International Conference on Machine Learning. PMLR: 3842–3853. arXiv:2010.11660.

businessweekly.co.uk

Quested, Tony. "Smartphones get smarter with Essex innovation". Business Weekly. Retrieved 2021-06-17.

clemenswinter.com

Winter, Clemens (2023-04-14). "Entity-Based Reinforcement Learning". Clemens Winter's Blog.

doi.org

Kaelbling, Leslie P.; Littman, Michael L.; Moore, Andrew W. (1996). "Reinforcement Learning: A Survey". Journal of Artificial Intelligence Research. 4: 237–285. arXiv:cs/9605103. doi:10.1613/jair.301. S2CID 1708582. Archived from the original on 2001-11-20.
van Otterlo, M.; Wiering, M. (2012). "Reinforcement Learning and Markov Decision Processes". Reinforcement Learning. Adaptation, Learning, and Optimization. Vol. 12. pp. 3–42. doi:10.1007/978-3-642-27645-3_1. ISBN 978-3-642-27644-6.
Li, Shengbo (2023). Reinforcement Learning for Sequential Decision and Optimal Control (First ed.). Springer Verlag, Singapore. pp. 1–460. doi:10.1007/978-981-19-7784-8. ISBN 978-9-811-97783-1. S2CID 257928563.{{cite book}}: CS1 maint: location missing publisher (link)
Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan (21 July 2012). "Neural Basis of Reinforcement Learning and Decision Making". Annual Review of Neuroscience. 35 (1): 287–308. doi:10.1146/annurev-neuro-062111-150512. PMC 3490621. PMID 22462543.
Salazar Duque, Edgar Mauricio; Giraldo, Juan S.; Vergara, Pedro P.; Nguyen, Phuong; Van Der Molen, Anne; Slootweg, Han (2022). "Community energy storage operation via reinforcement learning with eligibility traces". Electric Power Systems Research. 212. Bibcode:2022EPSR..21208515S. doi:10.1016/j.epsr.2022.108515. S2CID 250635151.
Vergara, Pedro P.; Salazar, Mauricio; Giraldo, Juan S.; Palensky, Peter (2022). "Optimal dispatch of PV inverters in unbalanced distribution systems using Reinforcement Learning". International Journal of Electrical Power & Energy Systems. 136. Bibcode:2022IJEPE.13607628V. doi:10.1016/j.ijepes.2021.107628. S2CID 244099841.
Ren, Yangang; Jiang, Jianhua; Zhan, Guojian; Li, Shengbo Eben; Chen, Chen; Li, Keqiang; Duan, Jingliang (2022). "Self-Learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections". IEEE Transactions on Intelligent Transportation Systems. 23 (12): 24145–24156. arXiv:2110.12359. doi:10.1109/TITS.2022.3196167.
Burnetas, Apostolos N.; Katehakis, Michael N. (1997), "Optimal adaptive policies for Markov Decision Processes", Mathematics of Operations Research, 22 (1): 222–255, doi:10.1287/moor.22.1.222, JSTOR 3690147
Singh, Satinder P.; Sutton, Richard S. (1996-03-01). "Reinforcement learning with replacing eligibility traces". Machine Learning. 22 (1): 123–158. doi:10.1007/BF00114726. ISSN 1573-0565.
Bradtke, Steven J.; Barto, Andrew G. (1996). "Learning to predict by the method of temporal differences". Machine Learning. 22: 33–57. CiteSeerX 10.1.1.143.857. doi:10.1023/A:1018056104778. S2CID 20327856.
Matzliach, Barouch; Ben-Gal, Irad; Kagan, Evgeny (2022). "Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities". Entropy. 24 (8): 1168. Bibcode:2022Entrp..24.1168M. doi:10.3390/e24081168. PMC 9407070. PMID 36010832.
Deisenroth, Marc Peter; Neumann, Gerhard; Peters, Jan (2013). A Survey on Policy Search for Robotics (PDF). Foundations and Trends in Robotics. Vol. 2. NOW Publishers. pp. 1–142. doi:10.1561/2300000021. hdl:10044/1/12051.
Lin, Long-Ji (1992). "Self-improving reactive agents based on reinforcement learning, planning and teaching" (PDF). Machine Learning volume 8. doi:10.1007/BF00992699.
Zou, Lan (2023-01-01), Zou, Lan (ed.), "Chapter 7 - Meta-reinforcement learning", Meta-Learning, Academic Press, pp. 267–297, doi:10.1016/b978-0-323-89931-4.00011-0, ISBN 978-0-323-89931-4, retrieved 2023-11-08
Grondman, Ivo; Vaandrager, Maarten; Busoniu, Lucian; Babuska, Robert; Schuitema, Erik (2012-06-01). "Efficient Model Learning Methods for Actor–Critic Control". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 42 (3): 591–602. doi:10.1109/TSMCB.2011.2170565. ISSN 1083-4419. PMID 22156998.
Riveret, Regis; Gao, Yang (2019). "A probabilistic argumentation framework for reinforcement learning agents". Autonomous Agents and Multi-Agent Systems. 33 (1–2): 216–274. doi:10.1007/s10458-019-09404-2. S2CID 71147890.
Thompson, Isaac Symes; Caron, Alberto; Hicks, Chris; Mavroudis, Vasilios (2024-11-07). "Entity-based Reinforcement Learning for Autonomous Cyber Defence". Proceedings of the Workshop on Autonomous Cybersecurity (AutonomousCyber '24). ACM. pp. 56–67. arXiv:2410.17647. doi:10.1145/3689933.3690835.
Dey, Somdip; Singh, Amit Kumar; Wang, Xiaohang; McDonald-Maier, Klaus (March 2020). "User Interaction Aware Reinforcement Learning for Power and Thermal Efficiency of CPU-GPU Mobile MPSoCs". 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE) (PDF). pp. 1728–1733. doi:10.23919/DATE48585.2020.9116294. ISBN 978-3-9819263-4-7. S2CID 219858480.
Kaplan, F.; Oudeyer, P. (2004). "Maximizing Learning Progress: An Internal Reward System for Development". In Iida, F.; Pfeifer, R.; Steels, L.; Kuniyoshi, Y. (eds.). Embodied Artificial Intelligence. Lecture Notes in Computer Science. Vol. 3139. Berlin; Heidelberg: Springer. pp. 259–270. doi:10.1007/978-3-540-27833-7_19. ISBN 978-3-540-22484-6. S2CID 9781221.
Klyubin, A.; Polani, D.; Nehaniv, C. (2008). "Keep your options open: an information-based driving principle for sensorimotor systems". PLOS ONE. 3 (12): e4018. Bibcode:2008PLoSO...3.4018K. doi:10.1371/journal.pone.0004018. PMC 2607028. PMID 19107219.
George Karimpanal, Thommen; Bouffanais, Roland (2019). "Self-organizing maps for storage and transfer of knowledge in reinforcement learning". Adaptive Behavior. 27 (2): 111–126. arXiv:1811.08318. doi:10.1177/1059712318818568. ISSN 1059-7123. S2CID 53774629.
J Duan; Y Guan; S Li (2021). "Distributional Soft Actor-Critic: Off-policy reinforcement learning for addressing value estimation errors". IEEE Transactions on Neural Networks and Learning Systems. 33 (11): 6584–6598. arXiv:2001.02811. doi:10.1109/TNNLS.2021.3082568. PMID 34101599. S2CID 211259373.
Y Ren; J Duan; S Li (2020). "Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic". 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). pp. 1–6. arXiv:2002.05502. doi:10.1109/ITSC45102.2020.9294300. ISBN 978-1-7281-4149-7. S2CID 211096594.
Duan, J; Wang, W; Xiao, L (2025). "Distributional Soft Actor-Critic with Three Refinements". IEEE Transactions on Pattern Analysis and Machine Intelligence. PP (5): 3935–3946. arXiv:2310.05858. doi:10.1109/TPAMI.2025.3537087. PMID 40031258.
Francois-Lavet, Vincent; et al. (2018). "An Introduction to Deep Reinforcement Learning". Foundations and Trends in Machine Learning. 11 (3–4): 219–354. arXiv:1811.12560. Bibcode:2018arXiv181112560F. doi:10.1561/2200000071. S2CID 54434537.
Mnih, Volodymyr; et al. (2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. S2CID 205242740.
Behzadan, Vahid; Munir, Arslan (2017). "Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks". Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science. Vol. 10358. pp. 262–275. arXiv:1701.04143. doi:10.1007/978-3-319-62416-7_19. ISBN 978-3-319-62415-0. S2CID 1562290.
Korkmaz, Ezgi (2022). "Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs". Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). 36 (7): 7229–7238. arXiv:2112.09025. doi:10.1609/aaai.v36i7.20684. S2CID 245219157.
Berenji, H.R. (1994). "Fuzzy Q-learning: A new approach for fuzzy dynamic programming". Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference. Orlando, FL, USA: IEEE. pp. 486–491. doi:10.1109/FUZZY.1994.343737. ISBN 0-7803-1896-X. S2CID 56694947.
Vincze, David (2017). "Fuzzy rule interpolation and reinforcement learning" (PDF). 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE. pp. 173–178. doi:10.1109/SAMI.2017.7880298. ISBN 978-1-5090-5655-2. S2CID 17590120.
Pitombeira-Neto, Anselmo R.; Santos, Helano P.; Coelho da Silva, Ticiana L.; de Macedo, José Antonio F. (March 2024). "Trajectory modeling via random utility inverse reinforcement learning". Information Sciences. 660: 120128. arXiv:2105.12092. doi:10.1016/j.ins.2024.120128. ISSN 0020-0255. S2CID 235187141.
Hayes C, Radulescu R, Bargiacchi E, et al. (2022). "A practical guide to multi-objective reinforcement learning and planning". Autonomous Agents and Multi-Agent Systems. 36. arXiv:2103.09568. doi:10.1007/s10458-022-09552-y. S2CID 254235920.,
Tamar, Aviv; Glassner, Yonatan; Mannor, Shie (2015-02-21). "Optimizing the CVaR via Sampling". Proceedings of the AAAI Conference on Artificial Intelligence. 29 (1). arXiv:1404.3862. doi:10.1609/aaai.v29i1.9561. ISSN 2374-3468.

elte.hu

people.inf.elte.hu

"Reinforcement learning: An introduction" (PDF). Archived from the original (PDF) on 2017-07-12. Retrieved 2017-07-23.

essex.ac.uk

repository.essex.ac.uk

Dey, Somdip; Singh, Amit Kumar; Wang, Xiaohang; McDonald-Maier, Klaus (March 2020). "User Interaction Aware Reinforcement Learning for Power and Thermal Efficiency of CPU-GPU Mobile MPSoCs". 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE) (PDF). pp. 1728–1733. doi:10.23919/DATE48585.2020.9116294. ISBN 978-3-9819263-4-7. S2CID 219858480.

handle.net

hdl.handle.net

Deisenroth, Marc Peter; Neumann, Gerhard; Peters, Jan (2013). A Survey on Policy Search for Robotics (PDF). Foundations and Trends in Robotics. Vol. 2. NOW Publishers. pp. 1–142. doi:10.1561/2300000021. hdl:10044/1/12051.

harvard.edu

ui.adsabs.harvard.edu

Salazar Duque, Edgar Mauricio; Giraldo, Juan S.; Vergara, Pedro P.; Nguyen, Phuong; Van Der Molen, Anne; Slootweg, Han (2022). "Community energy storage operation via reinforcement learning with eligibility traces". Electric Power Systems Research. 212. Bibcode:2022EPSR..21208515S. doi:10.1016/j.epsr.2022.108515. S2CID 250635151.
Vergara, Pedro P.; Salazar, Mauricio; Giraldo, Juan S.; Palensky, Peter (2022). "Optimal dispatch of PV inverters in unbalanced distribution systems using Reinforcement Learning". International Journal of Electrical Power & Energy Systems. 136. Bibcode:2022IJEPE.13607628V. doi:10.1016/j.ijepes.2021.107628. S2CID 244099841.
Matzliach, Barouch; Ben-Gal, Irad; Kagan, Evgeny (2022). "Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities". Entropy. 24 (8): 1168. Bibcode:2022Entrp..24.1168M. doi:10.3390/e24081168. PMC 9407070. PMID 36010832.
Kulkarni, Tejas D.; Narasimhan, Karthik R.; Saeedi, Ardavan; Tenenbaum, Joshua B. (2016). "Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation". Proceedings of the 30th International Conference on Neural Information Processing Systems. NIPS'16. USA: Curran Associates Inc.: 3682–3690. arXiv:1604.06057. Bibcode:2016arXiv160406057K. ISBN 978-1-5108-3881-9.
Klyubin, A.; Polani, D.; Nehaniv, C. (2008). "Keep your options open: an information-based driving principle for sensorimotor systems". PLOS ONE. 3 (12): e4018. Bibcode:2008PLoSO...3.4018K. doi:10.1371/journal.pone.0004018. PMC 2607028. PMID 19107219.
Francois-Lavet, Vincent; et al. (2018). "An Introduction to Deep Reinforcement Learning". Foundations and Trends in Machine Learning. 11 (3–4): 219–354. arXiv:1811.12560. Bibcode:2018arXiv181112560F. doi:10.1561/2200000071. S2CID 54434537.
Mnih, Volodymyr; et al. (2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. S2CID 205242740.

ieee.org

ieeexplore.ieee.org

Ren, Yangang; Jiang, Jianhua; Zhan, Guojian; Li, Shengbo Eben; Chen, Chen; Li, Keqiang; Duan, Jingliang (2022). "Self-Learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections". IEEE Transactions on Intelligent Transportation Systems. 23 (12): 24145–24156. arXiv:2110.12359. doi:10.1109/TITS.2022.3196167.
Dey, Somdip; Singh, Amit Kumar; Wang, Xiaohang; McDonald-Maier, Klaus (March 2020). "User Interaction Aware Reinforcement Learning for Power and Thermal Efficiency of CPU-GPU Mobile MPSoCs". 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE) (PDF). pp. 1728–1733. doi:10.23919/DATE48585.2020.9116294. ISBN 978-3-9819263-4-7. S2CID 219858480.
J Duan; Y Guan; S Li (2021). "Distributional Soft Actor-Critic: Off-policy reinforcement learning for addressing value estimation errors". IEEE Transactions on Neural Networks and Learning Systems. 33 (11): 6584–6598. arXiv:2001.02811. doi:10.1109/TNNLS.2021.3082568. PMID 34101599. S2CID 211259373.
Y Ren; J Duan; S Li (2020). "Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic". 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). pp. 1–6. arXiv:2002.05502. doi:10.1109/ITSC45102.2020.9294300. ISBN 978-1-7281-4149-7. S2CID 211096594.
Berenji, H.R. (1994). "Fuzzy Q-learning: A new approach for fuzzy dynamic programming". Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference. Orlando, FL, USA: IEEE. pp. 486–491. doi:10.1109/FUZZY.1994.343737. ISBN 0-7803-1896-X. S2CID 56694947.

incompleteideas.net

Sutton & Barto 2018, Chapter 11. Sutton, Richard S.; Barto, Andrew G. (2018) [1998]. Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN 978-0-262-03924-6.
Sutton, Richard S. (1984). Temporal Credit Assignment in Reinforcement Learning (PhD thesis). University of Massachusetts, Amherst, MA. Archived from the original on 2017-03-30. Retrieved 2017-03-29.
Sutton & Barto 2018, §6. Temporal-Difference Learning. Sutton, Richard S.; Barto, Andrew G. (2018) [1998]. Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN 978-0-262-03924-6.
cf. Sutton & Barto 2018, Section 5.4, p. 100 Sutton, Richard S.; Barto, Andrew G. (2018) [1998]. Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN 978-0-262-03924-6.

inews.co.uk

Williams, Rhiannon (2020-07-21). "Future smartphones 'will prolong their own battery life by monitoring owners' behaviour'". i. Retrieved 2021-06-17.

jmlr.org

García, Javier; Fernández, Fernando (1 January 2015). "A comprehensive survey on safe reinforcement learning" (PDF). The Journal of Machine Learning Research. 16 (1): 1437–1480.

jstor.org

Burnetas, Apostolos N.; Katehakis, Michael N. (1997), "Optimal adaptive policies for Markov Decision Processes", Mathematics of Operations Research, 22 (1): 222–255, doi:10.1287/moor.22.1.222, JSTOR 3690147

lincoln.ac.uk

eprints.lincoln.ac.uk

Deisenroth, Marc Peter; Neumann, Gerhard; Peters, Jan (2013). A Survey on Policy Search for Robotics (PDF). Foundations and Trends in Robotics. Vol. 2. NOW Publishers. pp. 1–142. doi:10.1561/2300000021. hdl:10044/1/12051.

loc.gov

webarchive.loc.gov

Kaelbling, Leslie P.; Littman, Michael L.; Moore, Andrew W. (1996). "Reinforcement Learning: A Survey". Journal of Artificial Intelligence Research. 4: 237–285. arXiv:cs/9605103. doi:10.1613/jair.301. S2CID 1708582. Archived from the original on 2001-11-20.

medium.com

Juliani, Arthur (2016-12-17). "Simple Reinforcement Learning with Tensorflow Part 8: Asynchronous Actor-Critic Agents (A3C)". Medium. Retrieved 2018-02-22.

mlr.press

proceedings.mlr.press

Dabney, Will; Ostrovski, Georg; Silver, David; Munos, Remi (2018-07-03). "Implicit Quantile Networks for Distributional Reinforcement Learning". Proceedings of the 35th International Conference on Machine Learning. PMLR: 1096–1105. arXiv:1806.06923.
Greenberg, Ido; Mannor, Shie (2021-07-01). "Detecting Rewards Deterioration in Episodic Reinforcement Learning". Proceedings of the 38th International Conference on Machine Learning. PMLR: 3842–3853. arXiv:2010.11660.

neurips.cc

proceedings.neurips.cc

van Hasselt, Hado; Hessel, Matteo; Aslanides, John (2019). "When to use parametric models in reinforcement learning?" (PDF). Advances in Neural Information Processing Systems 32.
Chow, Yinlam; Tamar, Aviv; Mannor, Shie; Pavone, Marco (2015). "Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach". Advances in Neural Information Processing Systems. 28. Curran Associates, Inc. arXiv:1506.02188.
Greenberg, Ido; Chow, Yinlam; Ghavamzadeh, Mohammad; Mannor, Shie (2022-12-06). "Efficient Risk-Averse Reinforcement Learning". Advances in Neural Information Processing Systems. 35: 32639–32652. arXiv:2205.05138.

nih.gov

pubmed.ncbi.nlm.nih.gov

Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan (21 July 2012). "Neural Basis of Reinforcement Learning and Decision Making". Annual Review of Neuroscience. 35 (1): 287–308. doi:10.1146/annurev-neuro-062111-150512. PMC 3490621. PMID 22462543.
Matzliach, Barouch; Ben-Gal, Irad; Kagan, Evgeny (2022). "Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities". Entropy. 24 (8): 1168. Bibcode:2022Entrp..24.1168M. doi:10.3390/e24081168. PMC 9407070. PMID 36010832.
Grondman, Ivo; Vaandrager, Maarten; Busoniu, Lucian; Babuska, Robert; Schuitema, Erik (2012-06-01). "Efficient Model Learning Methods for Actor–Critic Control". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 42 (3): 591–602. doi:10.1109/TSMCB.2011.2170565. ISSN 1083-4419. PMID 22156998.
Klyubin, A.; Polani, D.; Nehaniv, C. (2008). "Keep your options open: an information-based driving principle for sensorimotor systems". PLOS ONE. 3 (12): e4018. Bibcode:2008PLoSO...3.4018K. doi:10.1371/journal.pone.0004018. PMC 2607028. PMID 19107219.
J Duan; Y Guan; S Li (2021). "Distributional Soft Actor-Critic: Off-policy reinforcement learning for addressing value estimation errors". IEEE Transactions on Neural Networks and Learning Systems. 33 (11): 6584–6598. arXiv:2001.02811. doi:10.1109/TNNLS.2021.3082568. PMID 34101599. S2CID 211259373.
Duan, J; Wang, W; Xiao, L (2025). "Distributional Soft Actor-Critic with Three Refinements". IEEE Transactions on Pattern Analysis and Machine Intelligence. PP (5): 3935–3946. arXiv:2310.05858. doi:10.1109/TPAMI.2025.3537087. PMID 40031258.
Mnih, Volodymyr; et al. (2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. S2CID 205242740.

ncbi.nlm.nih.gov

Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan (21 July 2012). "Neural Basis of Reinforcement Learning and Decision Making". Annual Review of Neuroscience. 35 (1): 287–308. doi:10.1146/annurev-neuro-062111-150512. PMC 3490621. PMID 22462543.
Matzliach, Barouch; Ben-Gal, Irad; Kagan, Evgeny (2022). "Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities". Entropy. 24 (8): 1168. Bibcode:2022Entrp..24.1168M. doi:10.3390/e24081168. PMC 9407070. PMID 36010832.
Klyubin, A.; Polani, D.; Nehaniv, C. (2008). "Keep your options open: an information-based driving principle for sensorimotor systems". PLOS ONE. 3 (12): e4018. Bibcode:2008PLoSO...3.4018K. doi:10.1371/journal.pone.0004018. PMC 2607028. PMID 19107219.

openreview.net

Engstrom, Logan; Ilyas, Andrew; Santurkar, Shibani; Tsipras, Dimitris; Janoos, Firdaus; Rudolph, Larry; Madry, Aleksander (2019-09-25). "Implementation Matters in Deep RL: A Case Study on PPO and TRPO". ICLR.
Colas, Cédric (2019-03-06). "A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms". International Conference on Learning Representations. arXiv:1904.06979.

pbworks.com

umichrl.pbworks.com

"Reinforcement Learning / Successes of Reinforcement Learning". umichrl.pbworks.com. Retrieved 2017-08-06.

psu.edu

citeseerx.ist.psu.edu

Bradtke, Steven J.; Barto, Andrew G. (1996). "Learning to predict by the method of temporal differences". Machine Learning. 22: 33–57. CiteSeerX 10.1.1.143.857. doi:10.1023/A:1018056104778. S2CID 20327856.
Williams, Ronald J. (1987). "A class of gradient-estimating algorithms for reinforcement learning in neural networks". Proceedings of the IEEE First International Conference on Neural Networks. CiteSeerX 10.1.1.129.8871.

rhul.ac.uk

cs.rhul.ac.uk

Watkins, Christopher J.C.H. (1989). Learning from Delayed Rewards (PDF) (PhD thesis). King's College, Cambridge, UK.

scholar.google.com

"Train Hard, Fight Easy: Robust Meta Reinforcement Learning". scholar.google.com. Retrieved 2024-06-21.

sciencedirect.com

Zou, Lan (2023-01-01), Zou, Lan (ed.), "Chapter 7 - Meta-reinforcement learning", Meta-Learning, Academic Press, pp. 267–297, doi:10.1016/b978-0-323-89931-4.00011-0, ISBN 978-0-323-89931-4, retrieved 2023-11-08

semanticscholar.org

api.semanticscholar.org

Kaelbling, Leslie P.; Littman, Michael L.; Moore, Andrew W. (1996). "Reinforcement Learning: A Survey". Journal of Artificial Intelligence Research. 4: 237–285. arXiv:cs/9605103. doi:10.1613/jair.301. S2CID 1708582. Archived from the original on 2001-11-20.
Li, Shengbo (2023). Reinforcement Learning for Sequential Decision and Optimal Control (First ed.). Springer Verlag, Singapore. pp. 1–460. doi:10.1007/978-981-19-7784-8. ISBN 978-9-811-97783-1. S2CID 257928563.{{cite book}}: CS1 maint: location missing publisher (link)
Salazar Duque, Edgar Mauricio; Giraldo, Juan S.; Vergara, Pedro P.; Nguyen, Phuong; Van Der Molen, Anne; Slootweg, Han (2022). "Community energy storage operation via reinforcement learning with eligibility traces". Electric Power Systems Research. 212. Bibcode:2022EPSR..21208515S. doi:10.1016/j.epsr.2022.108515. S2CID 250635151.
Vergara, Pedro P.; Salazar, Mauricio; Giraldo, Juan S.; Palensky, Peter (2022). "Optimal dispatch of PV inverters in unbalanced distribution systems using Reinforcement Learning". International Journal of Electrical Power & Energy Systems. 136. Bibcode:2022IJEPE.13607628V. doi:10.1016/j.ijepes.2021.107628. S2CID 244099841.
Bradtke, Steven J.; Barto, Andrew G. (1996). "Learning to predict by the method of temporal differences". Machine Learning. 22: 33–57. CiteSeerX 10.1.1.143.857. doi:10.1023/A:1018056104778. S2CID 20327856.
Riveret, Regis; Gao, Yang (2019). "A probabilistic argumentation framework for reinforcement learning agents". Autonomous Agents and Multi-Agent Systems. 33 (1–2): 216–274. doi:10.1007/s10458-019-09404-2. S2CID 71147890.
Dey, Somdip; Singh, Amit Kumar; Wang, Xiaohang; McDonald-Maier, Klaus (March 2020). "User Interaction Aware Reinforcement Learning for Power and Thermal Efficiency of CPU-GPU Mobile MPSoCs". 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE) (PDF). pp. 1728–1733. doi:10.23919/DATE48585.2020.9116294. ISBN 978-3-9819263-4-7. S2CID 219858480.
Kaplan, F.; Oudeyer, P. (2004). "Maximizing Learning Progress: An Internal Reward System for Development". In Iida, F.; Pfeifer, R.; Steels, L.; Kuniyoshi, Y. (eds.). Embodied Artificial Intelligence. Lecture Notes in Computer Science. Vol. 3139. Berlin; Heidelberg: Springer. pp. 259–270. doi:10.1007/978-3-540-27833-7_19. ISBN 978-3-540-22484-6. S2CID 9781221.
George Karimpanal, Thommen; Bouffanais, Roland (2019). "Self-organizing maps for storage and transfer of knowledge in reinforcement learning". Adaptive Behavior. 27 (2): 111–126. arXiv:1811.08318. doi:10.1177/1059712318818568. ISSN 1059-7123. S2CID 53774629.
J Duan; Y Guan; S Li (2021). "Distributional Soft Actor-Critic: Off-policy reinforcement learning for addressing value estimation errors". IEEE Transactions on Neural Networks and Learning Systems. 33 (11): 6584–6598. arXiv:2001.02811. doi:10.1109/TNNLS.2021.3082568. PMID 34101599. S2CID 211259373.
Y Ren; J Duan; S Li (2020). "Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic". 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). pp. 1–6. arXiv:2002.05502. doi:10.1109/ITSC45102.2020.9294300. ISBN 978-1-7281-4149-7. S2CID 211096594.
Francois-Lavet, Vincent; et al. (2018). "An Introduction to Deep Reinforcement Learning". Foundations and Trends in Machine Learning. 11 (3–4): 219–354. arXiv:1811.12560. Bibcode:2018arXiv181112560F. doi:10.1561/2200000071. S2CID 54434537.
Mnih, Volodymyr; et al. (2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. S2CID 205242740.
Behzadan, Vahid; Munir, Arslan (2017). "Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks". Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science. Vol. 10358. pp. 262–275. arXiv:1701.04143. doi:10.1007/978-3-319-62416-7_19. ISBN 978-3-319-62415-0. S2CID 1562290.
Korkmaz, Ezgi (2022). "Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs". Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). 36 (7): 7229–7238. arXiv:2112.09025. doi:10.1609/aaai.v36i7.20684. S2CID 245219157.
Berenji, H.R. (1994). "Fuzzy Q-learning: A new approach for fuzzy dynamic programming". Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference. Orlando, FL, USA: IEEE. pp. 486–491. doi:10.1109/FUZZY.1994.343737. ISBN 0-7803-1896-X. S2CID 56694947.
Vincze, David (2017). "Fuzzy rule interpolation and reinforcement learning" (PDF). 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE. pp. 173–178. doi:10.1109/SAMI.2017.7880298. ISBN 978-1-5090-5655-2. S2CID 17590120.
Ziebart, Brian D.; Maas, Andrew; Bagnell, J. Andrew; Dey, Anind K. (2008-07-13). "Maximum entropy inverse reinforcement learning". Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 3. AAAI'08. Chicago, Illinois: AAAI Press: 1433–1438. ISBN 978-1-57735-368-3. S2CID 336219.
Pitombeira-Neto, Anselmo R.; Santos, Helano P.; Coelho da Silva, Ticiana L.; de Macedo, José Antonio F. (March 2024). "Trajectory modeling via random utility inverse reinforcement learning". Information Sciences. 660: 120128. arXiv:2105.12092. doi:10.1016/j.ins.2024.120128. ISSN 0020-0255. S2CID 235187141.
Hayes C, Radulescu R, Bargiacchi E, et al. (2022). "A practical guide to multi-objective reinforcement learning and planning". Autonomous Agents and Multi-Agent Systems. 36. arXiv:2103.09568. doi:10.1007/s10458-022-09552-y. S2CID 254235920.,

springer.com

link.springer.com

Li, Shengbo (2023). Reinforcement Learning for Sequential Decision and Optimal Control (First ed.). Springer Verlag, Singapore. pp. 1–460. doi:10.1007/978-981-19-7784-8. ISBN 978-9-811-97783-1. S2CID 257928563.{{cite book}}: CS1 maint: location missing publisher (link)
Singh, Satinder P.; Sutton, Richard S. (1996-03-01). "Reinforcement learning with replacing eligibility traces". Machine Learning. 22 (1): 123–158. doi:10.1007/BF00114726. ISSN 1573-0565.
Lin, Long-Ji (1992). "Self-improving reactive agents based on reinforcement learning, planning and teaching" (PDF). Machine Learning volume 8. doi:10.1007/BF00992699.

springer.com

Gosavi, Abhijit (2003). Simulation-based Optimization: Parametric Optimization Techniques and Reinforcement. Operations Research/Computer Science Interfaces Series. Springer. ISBN 978-1-4020-7454-7.

ssrn.com

papers.ssrn.com

Dabérius, Kevin; Granat, Elvin; Karlsson, Patrik (2020). "Deep Execution - Value and Policy Based Reinforcement Learning for Trading and Beating Market Benchmarks". The Journal of Machine Learning in Finance. 1. SSRN 3374766.

stanford.edu

ai.stanford.edu

Ng, A. Y.; Russell, S. J. (2000). "Algorithms for Inverse Reinforcement Learning" (PDF). Proceeding ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann Publishers. pp. 663–670. ISBN 1-55860-707-2.

tokic.com

Tokic, Michel; Palm, Günther (2011), "Value-Difference Based Exploration: Adaptive Control Between Epsilon-Greedy and Softmax" (PDF), KI 2011: Advances in Artificial Intelligence, Lecture Notes in Computer Science, vol. 7006, Springer, pp. 335–346, ISBN 978-3-642-24455-1

umass.edu

people.cs.umass.edu

Barto, A. G. (2013). "Intrinsic motivation and reinforcement learning". Intrinsically Motivated Learning in Natural and Artificial Systems (PDF). Berlin; Heidelberg: Springer. pp. 17–47.

web.cs.umass.edu

Bozinovski S. (1995) "Neuro genetic agents and structural theory of self-reinforcement learning systems". CMPSCI Technical Report 95-107, University of Massachusetts at Amherst [1]

uni-miskolc.hu

users.iit.uni-miskolc.hu

Vincze, David (2017). "Fuzzy rule interpolation and reinforcement learning" (PDF). 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE. pp. 173–178. doi:10.1109/SAMI.2017.7880298. ISBN 978-1-5090-5655-2. S2CID 17590120.

usc.edu

www-clmc.usc.edu

Peters, Jan; Vijayakumar, Sethu; Schaal, Stefan (2003). Reinforcement Learning for Humanoid Robotics (PDF). IEEE-RAS International Conference on Humanoid Robots. Archived from the original (PDF) on 2013-05-12.

washington.edu

cs.washington.edu

Kaelbling, Leslie P.; Littman, Michael L.; Moore, Andrew W. (1996). "Reinforcement Learning: A Survey". Journal of Artificial Intelligence Research. 4: 237–285. arXiv:cs/9605103. doi:10.1613/jair.301. S2CID 1708582. Archived from the original on 2001-11-20.

web.archive.org

"Reinforcement learning: An introduction" (PDF). Archived from the original (PDF) on 2017-07-12. Retrieved 2017-07-23.
Sutton, Richard S. (1984). Temporal Credit Assignment in Reinforcement Learning (PhD thesis). University of Massachusetts, Amherst, MA. Archived from the original on 2017-03-30. Retrieved 2017-03-29.
Peters, Jan; Vijayakumar, Sethu; Schaal, Stefan (2003). Reinforcement Learning for Humanoid Robotics (PDF). IEEE-RAS International Conference on Humanoid Robots. Archived from the original (PDF) on 2013-05-12.

worldcat.org

search.worldcat.org

Singh, Satinder P.; Sutton, Richard S. (1996-03-01). "Reinforcement learning with replacing eligibility traces". Machine Learning. 22 (1): 123–158. doi:10.1007/BF00114726. ISSN 1573-0565.
Grondman, Ivo; Vaandrager, Maarten; Busoniu, Lucian; Babuska, Robert; Schuitema, Erik (2012-06-01). "Efficient Model Learning Methods for Actor–Critic Control". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 42 (3): 591–602. doi:10.1109/TSMCB.2011.2170565. ISSN 1083-4419. PMID 22156998.
George Karimpanal, Thommen; Bouffanais, Roland (2019). "Self-organizing maps for storage and transfer of knowledge in reinforcement learning". Adaptive Behavior. 27 (2): 111–126. arXiv:1811.08318. doi:10.1177/1059712318818568. ISSN 1059-7123. S2CID 53774629.
Huang, Sandy; Papernot, Nicolas; Goodfellow, Ian; Duan, Yan; Abbeel, Pieter (2017-02-07). Adversarial Attacks on Neural Network Policies. OCLC 1106256905.
Pitombeira-Neto, Anselmo R.; Santos, Helano P.; Coelho da Silva, Ticiana L.; de Macedo, José Antonio F. (March 2024). "Trajectory modeling via random utility inverse reinforcement learning". Information Sciences. 660: 120128. arXiv:2105.12092. doi:10.1016/j.ins.2024.120128. ISSN 0020-0255. S2CID 235187141.
Tamar, Aviv; Glassner, Yonatan; Mannor, Shie (2015-02-21). "Optimizing the CVaR via Sampling". Proceedings of the AAAI Conference on Artificial Intelligence. 29 (1). arXiv:1404.3862. doi:10.1609/aaai.v29i1.9561. ISSN 2374-3468.

worldcat.org

Huang, Sandy; Papernot, Nicolas; Goodfellow, Ian; Duan, Yan; Abbeel, Pieter (2017-02-07). Adversarial Attacks on Neural Network Policies. OCLC 1106256905.