Comparative Analysis of Existing Architectures for General Game Agents

被引：1

作者：

Hosu, Ionel-Alexandru ^{[1
]}

Urzica, Andreea ^{[1
]}

机构：

[1] Univ Politehn Bucuresti, Fac Automat Control & Comp Sci, Bucharest, Romania

来源：

2015 17TH INTERNATIONAL SYMPOSIUM ON SYMBOLIC AND NUMERIC ALGORITHMS FOR SCIENTIFIC COMPUTING (SYNASC) | 2016年

关键词：

convolutional neural networks; Q-learning; model-free algorithms; neuroevolution; games;

D O I：

10.1109/SYNASC.2015.48

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper addresses the development of general purpose game agents able to learn a vast number of games using the same architecture. The article analyzes the main existing approaches to general game playing, reviews their performance and proposes future research directions. Methods such as deep learning, reinforcement learning and evolutionary algorithms are considered for this problem. The testing platform is the popular video game console Atari 2600. Research into developing general purpose agents for games is closely related to achieving artificial general intelligence (AGI).

引用

页码：257 / 260

页数：4

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