Memory-extraction-based DRL cooperative guidance against the maneuvering target protected by interceptors

This paper presents an open and interesting issue for missiles, i.e., achieving collaborative parameters constrained cooperative guidance, despite the interference of pursing interceptors (INTs) and the maneuvering target, by the fact that the target-missile-interceptor (TMI) engagement induces their complex and time-varying relationships. The Memory-Extraction-based Soft-Actor-Critic (ME-SAC) approach is proposed, which enhances the collaborative performance of missiles by implicitly extracting coupling motion characteristics among TMI from historical state, achieving the joint optimization of situation awareness and strategy. Firstly, the cooperative guidance task is formulated as a multi-order Markov decision process (MOMDP) to better represent the dynamic evolution of engagement, and a memory-extraction process is introduced to alleviate the curse of dimensionality. Secondly, a memory-decision-oriented maximum entropy framework combined with memory update modules is designed for enhancing strategy search ability. Then, a domain-knowledge-based pre-training is implemented to improve convergence speed. Finally, in simulation evaluation with various scenarios, the proposed ME-SAC shows more promising than the typical DRL-based and model-based algorithms in task success rate, learning efficiency, and adaptability.