Abstract:

Based on the Markov decision process theory, a reinforcement learning method for stealthy engagement strategy for air vehicles in 3D space is proposed. The advantage region and the exposure region for the environment modeling are established. In order to overcome the dimensional disaster problem, a Q-learning algorithm based on the radial basis function neural network (RBFNN) is put forward and a ranked sampling method is explained. Then simulations for two different situations are carried out，and the results show that the proposed algorithm is effective for the stealthy engagement strategy through reasonable ranked sampling methods.