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On the convergence of reinforcement learning

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Cited by:

  1. repec:plo:pone00:0208095 is not listed on IDEAS
  2. Fortini, Sandra & Petrone, Sonia & Sporysheva, Polina, 2018. "On a notion of partially conditionally identically distributed sequences," Stochastic Processes and their Applications, Elsevier, vol. 128(3), pages 819-846.
  3. Mele, Antonio & Molnár, Krisztina & Santoro, Sergio, 2020. "On the perils of stabilizing prices when agents are learning," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 339-353.
  4. Beggs, Alan, 2022. "Reference points and learning," Journal of Mathematical Economics, Elsevier, vol. 100(C).
  5. Oyarzun, Carlos & Ruf, Johannes, 2014. "Convergence in models with bounded expected relative hazard rates," Journal of Economic Theory, Elsevier, vol. 154(C), pages 229-244.
  6. Naoki Funai, 2019. "Convergence results on stochastic adaptive learning," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(4), pages 907-934, November.
  7. March, Christoph, 2019. "The behavioral economics of artificial intelligence: Lessons from experiments with computer players," BERG Working Paper Series 154, Bamberg University, Bamberg Economic Research Group.
  8. Maxwell Pak & Bing Xu, 2016. "Generalized reinforcement learning in perfect-information games," International Journal of Game Theory, Springer;Game Theory Society, vol. 45(4), pages 985-1011, November.
  9. Friedman, Daniel & Huck, Steffen & Oprea, Ryan & Weidenholzer, Simon, 2015. "From imitation to collusion: Long-run learning in a low-information environment," Journal of Economic Theory, Elsevier, vol. 155(C), pages 185-205.
  10. Manxi Wu & Saurabh Amin & Asuman Ozdaglar, 2021. "Multi-agent Bayesian Learning with Best Response Dynamics: Convergence and Stability," Papers 2109.00719, arXiv.org.
  11. Ianni, Antonella, 2014. "Learning strict Nash equilibria through reinforcement," Journal of Mathematical Economics, Elsevier, vol. 50(C), pages 148-155.
  12. Naoki Funai, 2013. "An Adaptive Learning Model in Coordination Games," Games, MDPI, vol. 4(4), pages 1-22, November.
  13. Karl D. Lewis & A. J. Shaiju, 2024. "Asymmetric Replicator Dynamics on Polish Spaces: Invariance, Stability, and Convergence," Dynamic Games and Applications, Springer, vol. 14(5), pages 1160-1190, November.
  14. Köke, Sonja & Lange, Andreas & Nicklisch, Andreas, 2015. "Adversity is a school of wisdomː Experimental evidence on cooperative protection against stochastic losses," WiSo-HH Working Paper Series 22, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
  15. Mertikopoulos, Panayotis & Sandholm, William H., 2024. "Nested replicator dynamics, nested logit choice, and similarity-based learning," Journal of Economic Theory, Elsevier, vol. 220(C).
  16. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
  17. Sonja Köke & Andreas Lange & Andreas Nicklisch, 2026. "Cooperative protection against stochastic losses: Experimental evidence on behavioral dynamics," Journal of Evolutionary Economics, Springer, vol. 36(2), pages 1-29, August.
  18. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
  19. Josephson, Jens, 2008. "A numerical analysis of the evolutionary stability of learning rules," Journal of Economic Dynamics and Control, Elsevier, vol. 32(5), pages 1569-1599, May.
  20. Ding, Jieyao & Nicklisch, Andreas, 2013. "On the impulse in impulse learning," Economics Letters, Elsevier, vol. 121(2), pages 294-297.
  21. Nicklisch, Andreas & Köke, Sonja & Lange, Andreas, 2016. "Is Adversity a School of Wisdom? Experimental Evidence on Cooperative Protection Against Stochastic Losses," VfS Annual Conference 2016 (Augsburg): Demographic Change 145716, Verein für Socialpolitik / German Economic Association.
  22. Chmura, Thorsten & Goerg, Sebastian J. & Selten, Reinhard, 2012. "Learning in experimental 2×2 games," Games and Economic Behavior, Elsevier, vol. 76(1), pages 44-73.
  23. Naoki Funai, 2013. "An Adaptive Learning Model in Coordination Games," Discussion Papers 13-14, Department of Economics, University of Birmingham.
  24. Ilaria Brunetti & Yezekael Hayel & Eitan Altman, 2018. "State-Policy Dynamics in Evolutionary Games," Dynamic Games and Applications, Springer, vol. 8(1), pages 93-116, March.
  25. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Gotts, Nicholas M. & Polhill, J. Gary, 2007. "Transient and asymptotic dynamics of reinforcement learning in games," Games and Economic Behavior, Elsevier, vol. 61(2), pages 259-276, November.
  26. Manxi Wu & Saurabh Amin, 2019. "Securing Infrastructure Facilities: When Does Proactive Defense Help?," Dynamic Games and Applications, Springer, vol. 9(4), pages 984-1025, December.
  27. Funai, Naoki, 2022. "Reinforcement learning with foregone payoff information in normal form games," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 638-660.
  28. Jacques Durieu & Philippe Solal, 2012. "Models of Adaptive Learning in Game Theory," Chapters, in: Richard Arena & Agnès Festré & Nathalie Lazaric (ed.), Handbook of Knowledge and Economics, chapter 11, Edward Elgar Publishing.
  29. Nazaria Solferino & Viviana Solferino & Serena F. Taurino, 2018. "The economics analysis of a Q-learning model of cooperation with punishment and risk taking preferences," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 601-613, October.
  30. Jieyao Ding & Andreas Nicklisch, 2013. "On the Impulse in Impulse Learning," Discussion Paper Series of the Max Planck Institute for Behavioral Economics 2013_02, Max Planck Institute for Behavioral Economics.
  31. Chernov, G. & Susin, I., 2019. "Models of learning in games: An overview," Journal of the New Economic Association, New Economic Association, vol. 44(4), pages 77-125.
  32. Mario Bravo, 2016. "An Adjusted Payoff-Based Procedure for Normal Form Games," Mathematics of Operations Research, INFORMS, vol. 41(4), pages 1469-1483, November.
  33. Enrique Fatas & Antonio J. Morales & Ainhoa Jaramillo-Gutiérrez, 2026. "Social aspiration reinforcement learning in Cournot games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 81(1), pages 485-524, February.
  34. Schuster, Stephan, 2010. "Network Formation with Adaptive Agents," MPRA Paper 27388, University Library of Munich, Germany.
  35. Georgios Chasparis & Jeff Shamma & Anders Rantzer, 2015. "Nonconvergence to saddle boundary points under perturbed reinforcement learning," International Journal of Game Theory, Springer;Game Theory Society, vol. 44(3), pages 667-699, August.
  36. Panayotis Mertikopoulos & William H. Sandholm, 2016. "Learning in Games via Reinforcement and Regularization," Mathematics of Operations Research, INFORMS, vol. 41(4), pages 1297-1324, November.
  37. Benoit Duvocelle & Panayotis Mertikopoulos & Mathias Staudigl & Dries Vermeulen, 2023. "Multiagent Online Learning in Time-Varying Games," Mathematics of Operations Research, INFORMS, vol. 48(2), pages 914-941, May.
  38. Masiliūnas, Aidas, 2023. "Learning in rent-seeking contests with payoff risk and foregone payoff information," Games and Economic Behavior, Elsevier, vol. 140(C), pages 50-72.
  39. Erik Mohlin & Robert Ostling & Joseph Tao-yi Wang, 2014. "Learning by Imitation in Games: Theory, Field, and Laboratory," Economics Series Working Papers 734, University of Oxford, Department of Economics.
  40. Mario Bravo & Mathieu Faure, 2013. "Reinforcement Learning with Restrictions on the Action Set," AMSE Working Papers 1335, Aix-Marseille School of Economics, France, revised 01 Jul 2013.
  41. Cominetti, Roberto & Melo, Emerson & Sorin, Sylvain, 2010. "A payoff-based learning procedure and its application to traffic games," Games and Economic Behavior, Elsevier, vol. 70(1), pages 71-83, September.
  42. Hopkins, Ed & Posch, Martin, 2005. "Attainability of boundary points under reinforcement learning," Games and Economic Behavior, Elsevier, vol. 53(1), pages 110-125, October.
  43. Roger Waldeck & Eric Darmon, 2006. "Can boundedly rational sellers learn to play Nash?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 1(2), pages 147-169, November.
  44. repec:esx:essedp:715 is not listed on IDEAS
  45. Leslie, David S. & Collins, E.J., 2006. "Generalised weakened fictitious play," Games and Economic Behavior, Elsevier, vol. 56(2), pages 285-298, August.
  46. Alanyali, Murat, 2010. "A note on adjusted replicator dynamics in iterated games," Journal of Mathematical Economics, Elsevier, vol. 46(1), pages 86-98, January.
  47. Manxi Wu & Saurabh Amin & Asuman Ozdaglar, 2025. "Convergence and Stability of Coupled Belief-Strategy Learning Dynamics in Continuous Games," Mathematics of Operations Research, INFORMS, vol. 50(1), pages 459-481, February.
  48. Hofbauer, Josef & Hopkins, Ed, 2005. "Learning in perturbed asymmetric games," Games and Economic Behavior, Elsevier, vol. 52(1), pages 133-152, July.
  49. Jaspersen, Johannes G. & Montibeller, Gilberto, 2020. "On the learning patterns and adaptive behavior of terrorist organizations," European Journal of Operational Research, Elsevier, vol. 282(1), pages 221-234.
  50. Albert Banal-Estañol & Augusto Rupérez Micola, 2009. "Composition of Electricity Generation Portfolios, Pivotal Dynamics, and Market Prices," Management Science, INFORMS, vol. 55(11), pages 1813-1831, November.
  51. Norman, Thomas W.L., 2023. "Pigouvian algorithmic platform design," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 322-332.
  52. Giacomo Aletti & Caterina May & Piercesare Secchi, 2012. "A Functional Equation Whose Unknown is $\mathcal{P}([0,1])$ Valued," Journal of Theoretical Probability, Springer, vol. 25(4), pages 1207-1232, December.
  53. Filippo Massari & Jonathan Newton, 2026. "Rational beliefs when the truth is not an option," International Journal of Game Theory, Springer;Game Theory Society, vol. 55(1), pages 1-26, June.
  54. Han, Jungsuk & Sangiorgi, Francesco, 2018. "Searching for information," Journal of Economic Theory, Elsevier, vol. 175(C), pages 342-373.
  55. Kuang Xu & Se-Young Yun, 2020. "Reinforcement with Fading Memories," Mathematics of Operations Research, INFORMS, vol. 45(4), pages 1258-1288, November.
  56. Pemantle, Robin & Skyrms, Brian, 2004. "Network formation by reinforcement learning: the long and medium run," Mathematical Social Sciences, Elsevier, vol. 48(3), pages 315-327, November.
  57. Michael Foley & Rory Smead & Patrick Forber & Christoph Riedl, 2021. "Avoiding the bullies: The resilience of cooperation among unequals," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-19, April.
  58. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.
  59. Georgios Chasparis & Jeff Shamma, 2012. "Distributed Dynamic Reinforcement of Efficient Outcomes in Multiagent Coordination and Network Formation," Dynamic Games and Applications, Springer, vol. 2(1), pages 18-50, March.
  60. Conor Mayo-Wilson & Kevin Zollman & David Danks, 2013. "Wisdom of crowds versus groupthink: learning in groups and in isolation," International Journal of Game Theory, Springer;Game Theory Society, vol. 42(3), pages 695-723, August.
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