Reinforcement learning in multi-agent environment and ant colony for packet scheduling in routers

Authors:
Malika Bourenane;Abdelhamid Mellouk;Djilali Benhamamouch
Affiliations:
University of Es-Senia, Oran, Algeria;University of Paris XII-Val de Marne, Paris, France;University of Es-Senia, Oran, Algeria
Venue:
Proceedings of the 5th ACM international workshop on Mobility management and wireless access
Year:
2007

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Abstract

The packet scheduling in router plays an important role in the sense to achieve QoS differentiation and to optimize the queuing delay, in particular when this optimization is accomplished on all routers of a path between source and destination. In a dynamically changing environment a good scheduling discipline should be also adaptive to the new traffic conditions. To solve this problem we use a multi-agent system in which each agent tries to optimize its own behaviour and communicate with other agents to make global coordination possible. This communication is done by mobile agents. In this paper, we adopt the framework of Markov decision processes applied to multi-agent system and present a pheromone-Q learning approach which combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents.