Computer Science ›› 2022, Vol. 49 ›› Issue (8): 191-204.doi: 10.11896/jsjkx.220200174

• Artificial Intelligence • Previous Articles     Next Articles

Methods in Adversarial Intelligent Game:A Holistic Comparative Analysis from Perspective of Game Theory and Reinforcement Learning

YUAN Wei-lin, LUO Jun-ren, LU Li-na, CHEN Jia-xing, ZHANG Wan-peng, CHEN Jing   

  1. College of Intelligence Science and Technology,National University of Defense Technology,Changsha 410073,China
  • Received:2022-02-27 Revised:2022-03-22 Published:2022-08-02
  • About author:YUAN Wei-lin,born in 1994,Ph.D candidate.His main research interests include agent modelling,adversarial team game and multi-agent reinforcement learning.
    LU Li-na,born in 1984,Ph.D.Her main research interests include hierarchical multi-agent system,reinforcement lear-ning and complex network.
  • Supported by:
    National Natural Science Foundation of China(61702528,61806212,62173336).

Abstract: Adversarial intelligent game is an advanced research in decision-making problem of intelligence cognitive.With the support of large computing power,game theory and reinforcement learning represented by counterfactual regret minimization and fictitious self-play respectively,are state-of-the-art approaches in searching strategies.However,the relationship between these two paradigms is not entirely explored.For adversarial intelligent game problems,this paper defines the connotation and extension of adversarial intelligent game,studies the development history of adversarial intelligent game,and summarizes the key challenges.From the perspectives of game theory and reinforcement learning,the models and algorithms of intelligent game are introduced.This paper conducts a comparative study from game theory and reinforcement learning,including the methods and framework,the main purpose is to promote the advance of intelligent game,and lay a foundation for the development of general artificial intelligence.

Key words: Adversarial intelligent game, Counterfactual regret minimization, Fictitious self-play, Nash equilibrium, Reinforcement learning

CLC Number: 

  • TP181
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