Application of Reinforcement Learning in Missile Guidance
强化学习在导弹制导中的应用

Principle and characteristic of reinforcement learning are outlined. The value function approximation of reinforcement learning with neural networks is studied, and the learning algorithm using modular neural networks to approximate the value function is emphatically analyzed, which decomposes the state space automatically and increases the generalizing ability of the neural networks. The neural networks are trained offline, and is used online as a feedback controller. The A learning algorithm is applied in the missile guidance problem, and the simulation results show the good performance and effectiveness of the application of reinforcement learning in those problems of missile guidance and control.