Conference Paper

A Cooperation Model Using Reinforcement Learning for Multi-agent.

DOI: 10.1007/11751649_74 Conference: Computational Science and Its Applications - ICCSA 2006, International Conference, Glasgow, UK, May 8-11, 2006, Proceedings, Part V
Source: DBLP


In multi-agent systems, the common goals of each agent are established and the problems are solved through cooperation and
control among agents. Because each agent performs parallel processes in a multi-agent system, this approach can be easily
applied to problems requiring parallel processing. The parallel processing prevents system performance degradation due to
local error operation in the system. It also can reduce the solution time when the problem is divided into several sub-problems.
In this case, each agent is designed independently providing a relatively simple programming model for solution of the problem.
Further, the system can be easily expanded by adding new function agents. In the study of multi-agent systems, the main research
topic is the coordination and cooperation among agents.

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