Colearning in Differential Games

被引：0
作者：
John W. Sheppard
机构：
[1] ARINC,
来源：
Machine Learning | 1998年 / 33卷
关键词：
Markov games; differential games; pursuit games; multiagent learning; reinforcement learning; Q-learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Game playing has been a popular problem area for research in artificial intelligence and machine learning for many years. In almost every study of game playing and machine learning, the focus has been on games with a finite set of states and a finite set of actions. Further, most of this research has focused on a single player or team learning how to play against another player or team that is applying a fixed strategy for playing the game. In this paper, we explore multiagent learning in the context of game playing and develop algorithms for “co-learning” in which all players attempt to learn their optimal strategies simultaneously. Specifically, we address two approaches to colearning, demonstrating strong performance by a memory-based reinforcement learner and comparable but faster performance with a tree-based reinforcement learner.
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页码：201 / 233
页数：32
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