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In the last decade, due to emerging real-time and multimedia applications, there has been much interest for developing mechanisms to take into account the quality of service required by these applications. We have proposed earlier an approach used an adaptive algorithm for packet routing using reinforcement learning called K-optimal path Q-routing algorithm (KOQRA) which optimizes simultaneously two additive QoS criteria: cumulative cost path and end-to-end delay. The approach developed here adds a new module to KOQRA dealing with the packet scheduling topic in order to achieve QoS differentiation and to optimize the queuing delay in a dynamically wireless changing environment. This module uses 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 process 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. Numerical results obtained with OPNET simulator for different levels of trafficpsilas load show that adaptive scheduling improves clearly performances of our earlier KOQRA.