Multi-agent reinforcement learning and chimpanzee hunting

Authors:
Michael Z. Sauter;Dongqing Shi;Jerald D. Kralik
Affiliations:
Department of Psychological and Brain Science, Dartmouth College, Hanover, NH;Department of Psychological and Brain Science, Dartmouth College, Hanover, NH;Department of Psychological and Brain Science, Dartmouth College, Hanover, NH
Venue:
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Year:
2009

Citing 5
Cited 1

Multiagent learning using a variable learning rate

Artificial Intelligence
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Designing Social Cognition Models for Multi-Agent Systems through Simulating Primate Societies

ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Distributed, heterogeneous, multi-agent social coordination via reinforcement learning

ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Value-function reinforcement learning in Markov games

Cognitive Systems Research

Distributed, heterogeneous, multi-agent social coordination via reinforcement learning

ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.