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Due to emerging real-time and multimedia applications, efficient routing ofinformation packets in dynamically changing communication network requires thatas the load levels, traffic patterns and topology of the network change, therouting policy also adapts. We focused in this paper on QoS based routing bydeveloping a neuro-dynamic programming to construct dynamic state dependentrouting policies. We propose an approach based on adaptive algorithm for packetrouting using reinforcement learning which optimizes two criteria: cumulativecost path and end-to-end delay. Numerical results obtained with OPNET simulatorfor different packet interarrival times statistical distributions withdifferent levels of traffic's load show that the proposed approach gives betterresults compared to standard optimal path routing algorithms.

Une approche modulaire probabiliste pour le routage \`a Qualit\'e de Service int\'egr\'ee