Abstract: Multiagent learning has become one of the rapidest development fields in AI and machine learning in recent years. In this paper, a multiagent dynamic cooperating model is proposed, which connects reinforcement learning with the BDI model. Notions of individual optimality loses its meaning since each agent’s payoff depends not only on iteself but also on other agent’s choices. The model adopts AFS NN to compress input state space, which can improve the reinforcement learning’s convergence velocity. At the same time, the simulated annealing algorithm is applied to heuristically point the search direction of action space. It can leap out the local minimum point and avoid the infinite growth of the iteration step number. Theory analysis and success in the robot soccer domain have proved the effectiveness of the multiagent dynamic cooperating model based on the BDI framework.