Maximizing learning progress: An internal reward system for development

被引：0

作者：

Kaplan, F ^{[1
]}

Oudeyer, PY ^{[1
]}

机构：

[1] Sony Comp Sci Lab Paris, Dev Robot Grp, F-75005 Paris, France

来源：

EMBODIED ARTIFICIAL INTELLIGENCE | 2004年 / 3139卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This chapter presents a generic internal reward system that drives an agent to increase the complexity of its behavior. This reward system does not reinforce a predefined task. Its purpose is to drive the agent to progress in learning given its embodiment and the environment in which it is placed. The dynamics created by such a system are studied first in a simple environment and then in the context of active vision.

引用

页码：259 / 270

页数：12

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