%0 Journal Article
%T Hybrid Multiagent Reinforcement Learning Approach: The Pursuit Problem
%A Zhou Pu-Cheng
%A Hong Bing-Rong
%A Huang Qing-Cheng
%A Javaid Khurshid
%J Information Technology Journal
%D 2006
%I Asian Network for Scientific Information
%X How to coordinate the behaviors of the agents through learning is a challenging problem within cooperative multi-agent domains. As a generic machine learning method, reinforcement learning can suit the needs of multi-agent learning in many aspects. This study presents a novel approach, which using reinforcement learning to learn the coordinated actions of a group of cooperative agents, without requiring explicit communication among them. The proposed approach combines advantages of the modular architecture, profit-sharing learning and opponent modeling technique in a single multi-agent framework. The effectiveness of the technique is demonstrated using the pursuit problem.
%K Multi-agent learning
%K Q-learning
%K profit-sharing learning
%K modular architecture
%K opponent modeling
%U http://docsdrive.com/pdfs/ansinet/itj/2006/1006-1011.pdf