Automatically designing counterfactual regret minimization algorithms for solving imperfect-information games
Authors:
- Kai LiKLKai Li
- Hang XuHXHang Xu
- Haobo FuHFHaobo Fu
- Qiang FuQFQiang Fu
- Junliang XingJXJunliang Xing
- 11 October 2024
- journal article
- research article
- Published by Elsevier in Artificial Intelligence
- Vol. 337
Abstract
No abstract availableKeywords
Funding Information
- Jiangsu Provincial Key Research and Development Program
- National Science and Technology Major Project
This publication has 12 references indexed in Scilit:
- Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture SearchACM Computing Surveys, 2024
- Student of Games: A unified learning algorithm for both perfect and imperfect information gamesScience Advances, 2023
- AutoML: A survey of the state-of-the-artKnowledge-Based Systems, 2021
- Superhuman AI for heads-up no-limit poker: Libratus beats top professionalsScience, 2018
- DeepStack: Expert-level artificial intelligence in heads-up no-limit pokerScience, 2017
- Mastering the game of Go with deep neural networks and tree searchNature, 2016
- Heads-up limit hold’em poker is solvedScience, 2015
- Deep BlueArtificial Intelligence, 2002
- A Simple Adaptive Procedure Leading to Correlated EquilibriumEconometrica, 2000
- Equilibrium points in n -person gamesProceedings of the National Academy of Sciences, 1950