Robustness and Sample Complexity of Model-Based MARL for General-Sum Markov Games-Reference-Cited by-同舟云学术

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Robustness and Sample Complexity of Model-Based MARL for General-Sum Markov Games

Published:2023-01-21 Issue: Volume: Page:
ISSN:2153-0785
Container-title:Dynamic Games and Applications
language:en
Short-container-title:Dyn Games Appl

Author:

Subramanian Jayakumar^ORCID,Sinha Amit,Mahajan Aditya^ORCID

Funder

Canadian Department of National Defence

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Statistics and Probability,Economics and Econometrics

Link

https://link.springer.com/content/pdf/10.1007/s13235-023-00490-2.pdf

Reference65 articles.

1. Acemoglu D, Robinson JA (2001) A theory of political transitions. Am Econ Rev 91(4):938–963

2. Agarwal A, Kakade S, Yang LF (2020) Model-based reinforcement learning with a generative model is minimax optimal. In Conference on Learning Theory, pages 67–83. PMLR

3. Aguirregabiria V, Mira P (2007) Sequential estimation of dynamic discrete games. Econometrica 75(1):1–53

4. Akchurina N (2010) Multi-agent reinforcement learning algorithms. PhD thesis, University of Paderborn

5. Albright SC, Winston W (1979) A birth-death model of advertising and pricing. Adv Appl Probab 11(1):134–152

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1. Special Issue : Multi-Agent Dynamic Decision Making and Learning;Dynamic Games and Applications;2023-01-31

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