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
收藏
页码：259 / 270
页数：12
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