智能博弈对抗方法:博弈论与强化学习综合视角对比分析

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

智能博弈对抗是人工智能认知决策领域亟待解决的前沿热点问题。以反事实后悔最小化算法为代表的博弈论方法和以虚拟自博弈算法为代表的强化学习方法,依托大规模算力支撑,在求解智能博弈策略中脱颖而出,但对两种范式之间的关联缺乏深入发掘。文中针对智能博弈对抗问题,定义智能博弈对抗的内涵与外延,梳理智能博弈对抗的发展历程,总结其中的关键挑战。从博弈论和强化学习两种视角出发,介绍智能博弈对抗模型、算法。多角度对比分析博弈理论和强化学习的优势与局限,归纳总结博弈理论与强化学习统一视角下的智能博弈对抗方法和策略求解框架,旨在为两种范式的结合提供方向,推动智能博弈技术前向发展,为迈向通用人工智能蓄力。

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.