Globally Optimal Multi-agent Reinforcement Learning Parameters in Distributed Task Assignment
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
A note on the learning effect in multi-agent optimization
Expert Systems with Applications: An International Journal
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Routing is an important issue in designing modern communication environments as well as in working of distributed systems. This paper concerns a concept of using Reinforcement Learning in the case of routing in computer networks. For this problem we propose a multi-agent approach called Modified Q-learning Routing. The proposed idea is compared with actually working solutions: distance-vector and deterministic routing. The properties and some experiments are presented, and it is shown that the proposed algorithm achieves better results than its opponents.