publication . Preprint . Conference object . Other literature type . 2018

Aspiration-based Perturbed Learning Automata

Chasparis, Georgios;
Open Access English
  • Published: 01 Mar 2018
Abstract
Comment: arXiv admin note: text overlap with arXiv:1709.05859, arXiv:1702.08334
Subjects
arXiv: Computer Science::Computer Science and Game Theory
ACM Computing Classification System: TheoryofComputation_GENERAL
free text keywords: Learning automata, distributed optimization, coordination games, Computer Science - Computer Science and Game Theory
Funded by
EC| RePhrase
Project
RePhrase
REfactoring Parallel Heterogeneous Resource-Aware Applications - a Software Engineering Approach
  • Funder: European Commission (EC)
  • Project Code: 644235
  • Funding stream: H2020 | RIA
Download fromView all 5 versions
ZENODO
Conference object . 2018
Provider: ZENODO
Zenodo
Other literature type . 2018
Provider: Datacite
Zenodo
Other literature type . 2018
Provider: Datacite
17 references, page 1 of 2

[1] W. B. Arthur, “On designing economic agents that behave like human agents,” J. Evolutionary Econ., vol. 3, pp. 1-22, 1993.

[2] M. Tsetlin, Automaton Theory and Modeling of Biological Systems. Academic Press, 1973.

[3] K. Narendra and M. Thathachar, Learning Automata: An introduction. Prentice-Hall, 1989.

[4] J. Hu and M. P. Wellman, “Nash Q-learning for general-sum stochastic games,” J. Machine Learning Research, vol. 4, no. Nov, pp. 1039- 1069, 2003.

[5] M. Thathachar and P. Sastry, Networks of Learning Automata: Techniques for Online Stochastic Optimization. Kluwer Academic Publishers, 2004.

[6] G. Chasparis and J. Shamma, “Distributed dynamic reinforcement of efficient outcomes in multiagent coordination and network formation,” Dynamic Games and Applications, vol. 2, no. 1, pp. 18-50, 2012.

[7] G. Chasparis, A. Arapostathis, and J. Shamma, “Aspiration learning in coordination games,” SIAM J. Control and Optim., vol. 51, no. 1, 2013.

[8] G. C. Chasparis, “Stochastic Stability of Perturbed Learning Automata in Positive-Utility Games,” arXiv:1709.05859 [cs], Sept. 2017. [OpenAIRE]

[9] G. C. Chasparis, J. S. Shamma, and A. Rantzer, “Nonconvergence to saddle boundary points under perturbed reinforcement learning,” Int. J. Game Theory, vol. 44, no. 3, pp. 667-699, 2015. [OpenAIRE]

[10] J. C. Harsanyi and R. Selten, A General Theory of Equilibrium Selection in Games. Cambridge, MA: MIT Press, 1988.

[11] J. R. Marden, H. P. Young, G. Arslan, and J. S. Shamma, “Payoff based dynamics for multi-player weakly acyclic games,” SIAM J. Control Optim., vol. 48, no. 1, pp. 373-396, 2009.

[12] J. R. Marden, H. P. Young, and L. Pao, “Achieving Pareto optimality through distributed learning,” SIAM J. Control Optim., vol. 52, no. 5, pp. 2753-2770, 2014.

[13] H. P. Young, “Learning by trial and error,” Games and Economic Behavior, vol. 65, no. 2, pp. 626-643, Mar. 2009.

[14] E. Hopkins and M. Posch, “Attainability of boundary points under reinforcement learning,” Games Econ. Behav., vol. 53, pp. 110-125, 2005.

[15] H. P. Young, Strategic Learning and Its Limits. New York, NY: Oxford University Press, 2004.

17 references, page 1 of 2
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue