Reinforcement learning guidance law for maneuvering target interception based on imitation learning

The advancement of maneuver penetration technology necessitates an improvement in the design of interception guidance laws to a higher level. An echelon intelligent guidance framework of “terminal guidance law model based on imitation learning (IL) → terminal guidance law evolution model based on reinforcement learning” is proposed to increase the interception probability, decrease the energy consumption, and improve the robustness of the guidance law for intercepting maneuvering targets. Firstly, a three-dimensional uncertain confrontation model between the maneuvering target and interceptor is established based on the interception collision triangle. Secondly, the IL method is used to mine the proportional navigation guidance (PNG) law, which provides a good initial policy for the subsequent reinforcement learning guidance law. Finally, a Markov decision model is established, and a process reward of energy consumption and a “soft” terminal reward model, including a “transition section,” are proposed. The proximal policy optimization (PPO) algorithm is used to fully explore the high-performance interception strategy. The findings of the Monte Carlo simulation show that the new guidance law is very stable and resilient, outperforming the conventional guidance algorithm in terms of interception probability and energy usage. Additionally, the single decision time is only 0.32 ms, making it of certain engineering value.