@inproceedings{dbf07aded27a4a30af87ac3064e322ef,
title = "High-level reinforcement learning in strategy games",
abstract = "Video games provide a rich testbed for artificial intelligence methods. In particular, creating automated opponents that perform well in strategy games is a difficult task. For instance, human players rapidly discover and exploit the weaknesses of hard coded strategies. To build better strategies, we suggest a reinforcement learning approach for learning a policy that switches between high-level strategies. These strategies are chosen based on different game situations and a fixed opponent strategy. Our learning agents are able to rapidly adapt to fixed opponents and improve deficiencies in the hard coded strategies, as the results demonstrate.",
keywords = "Reinforcement Learning, Video games, Virtual agents",
author = "Christopher Amato and Guy Shani",
year = "2010",
month = jan,
day = "1",
language = "English",
isbn = "9781617387715",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "75--82",
booktitle = "9th International Joint Conference on Autonomous Agents and Multiagent Systems 2010, AAMAS 2010",
note = "9th International Joint Conference on Autonomous Agents and Multiagent Systems 2010, AAMAS 2010 ; Conference date: 10-05-2010",
}