We present a new algorithm for cooperative reinforcement learning in multiagent systems. Our main concern is the correct coordination between the members of the team : We seek to obtain an optimal solution for the team as a whole while keeping the learning as much decentralized as possible. We consider autonomous and independently learning agents that do not store any explicit information about their teammates' behavior, as well as possibly different reward functions for each agent. Coordination between agents occurs through communication, namely the mutual notification algorithm.