Finding nash equilibrium for imperfect information games via fictitious play based on local regret minimization-Reference-Cited by-同舟云学术

Finding nash equilibrium for imperfect information games via fictitious play based on local regret minimization

Published:2022-02 Issue:9 Volume:37 Page:6152-6167
ISSN:0884-8173
Container-title:International Journal of Intelligent Systems
language:en
Short-container-title:Int J of Intelligent Sys

Author:

He Kangxin¹,Wu Haolin¹,Wang Zhuang²,Li Hui¹³

Affiliation:

1. College of Computer Science Sichuan University Chengdu China

2. College of Air Traffic Management Civil Aviation Flight University of China Guanghan China

3. National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China

Publisher

Hindawi Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22837

Reference35 articles.

1. SilverD HubertT SchrittwieserJ et al. Mastering chess and shogi by self‐play with a general reinforcement learning algorithm. arXiv preprint arXiv:1712.01815.2017.

2. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

3. Fast algorithms for finding randomized strategies in game trees

4. Iterative solution of games by fictitious play;Brown GW;Activity Anal Prod Allocation,1951

5. HeinrichJ SilverD.Deep reinforcement learning from self‐play in imperfect‐information games. arXiv preprint arXiv:1603.01121.2016.

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