基于强化学习的数字格斗游戏自主代理的开发

2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames) Pub Date : 2020-11-01 DOI:10.1109/SBGames51465.2020.00017

J. R. Bezerra, L. F. Góes, Alysson Ribeiro Da Silva

{"title":"基于强化学习的数字格斗游戏自主代理的开发","authors":"J. R. Bezerra, L. F. Góes, Alysson Ribeiro Da Silva","doi":"10.1109/SBGames51465.2020.00017","DOIUrl":null,"url":null,"abstract":"In this work, an autonomous agent based on reinforcement learning is implemented in a digital fighting game. The implemented agent uses Fusion Architecture for Learning, COgnition, and Navigation (FALCON) and Associative Resonance Map (ARAM) neural networks. The experimental results show that the autonomous agent is able to develop game strategies using the experience acquired in the matches, and achieves a winning rate of up to 90% against an agent with fixed behavior.","PeriodicalId":335816,"journal":{"name":"2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Development of an Autonomous Agent based on Reinforcement Learning for a Digital Fighting Game\",\"authors\":\"J. R. Bezerra, L. F. Góes, Alysson Ribeiro Da Silva\",\"doi\":\"10.1109/SBGames51465.2020.00017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, an autonomous agent based on reinforcement learning is implemented in a digital fighting game. The implemented agent uses Fusion Architecture for Learning, COgnition, and Navigation (FALCON) and Associative Resonance Map (ARAM) neural networks. The experimental results show that the autonomous agent is able to develop game strategies using the experience acquired in the matches, and achieves a winning rate of up to 90% against an agent with fixed behavior.\",\"PeriodicalId\":335816,\"journal\":{\"name\":\"2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SBGames51465.2020.00017\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBGames51465.2020.00017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

在这项工作中，基于强化学习的自主智能体在数字格斗游戏中实现。实现的智能体使用学习、认知和导航的融合架构(FALCON)和关联共振图(ARAM)神经网络。实验结果表明，自主智能体能够利用在比赛中获得的经验制定博弈策略，并在与固定行为的智能体的对抗中取得高达90%的胜率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Development of an Autonomous Agent based on Reinforcement Learning for a Digital Fighting Game

In this work, an autonomous agent based on reinforcement learning is implemented in a digital fighting game. The implemented agent uses Fusion Architecture for Learning, COgnition, and Navigation (FALCON) and Associative Resonance Map (ARAM) neural networks. The experimental results show that the autonomous agent is able to develop game strategies using the experience acquired in the matches, and achieves a winning rate of up to 90% against an agent with fixed behavior.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)

自引率

0.00%

发文量