Multi-Agent Reinforcement Learning and Chimpanzee Hunting

被引：3
|
作者：
Sauter, Michael Z. [1 ]
Shi, Dongqing [1 ]
Kralik, Jerald D. [1 ]
机构：
[1] Dartmouth Coll, Dept Psychol & Brain Sci, Hanover, NH 03755 USA
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO 2009), VOLS 1-4 | 2009年
关键词：
D O I：
10.1109/ROBIO.2009.5420602
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The use of multi-agent reinforcement learning is growing because of it's ability to scale in complexity and its lack of need for knowledge of the state and other agents. Chimpanzee hunting behavior is a suitable complex and interesting model for which multi-agent reinforcement learning is appropriate. Chimpanzee hunting strategies vary in both use and complexity and ultimately depend on the environment for which they are applied. Learning to use the varying strategies and learning when they are most effective is what this paper addresses and provides initial results and framework to build upon.
引用
收藏
页码：622 / 626
页数：5
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