For the complex problems of multi-aircraft cooperative game guidance with No-Fly Zone (NFZ) avoidance and cross-task constraint propagation, a deep deterministic policy gradient algorithm with temporal awareness and priority cooperative optimization (TP-MADDPG) is proposed. Based on the three-body cooperative guidance, a new coupled guidance task is formed by adding the NFZ avoidance constraint. At the same time, considering the constraint compatibility problem in dynamic task switching, the cooperative aircraft are modeled as independent agents with differentiated policy networks. First, a nonlinear kinematic model of the three-body game constructed by Evader–Pursuer–Defender is established. And four complex constraint conditions, namely homing guidance, NFZ avoidance, collision avoidance, and cooperative guidance, are modeled separately. Secondly, the Long Short-Term Memory-based (LSTM) Actor–Critic framework is proposed to dynamically capture the evolution patterns of adversarial scenarios by mining hidden correlations in historical state-action sequences. This enables smooth policy transitions between the cooperative guidance phase and subsequent homing guidance phase, effectively addressing the challenges of environmental non-stationarity and temporal task dependencies. Then, a priority-driven adaptive sampling mechanism is proposed along with a heterogeneous roles cooperative reward function to specifically address credit assignment imbalance and sparse reward problems, respectively. The sampling mechanism capitalizes on the efficient retrieval properties of SumTree data structures while integrating bias correction techniques to expedite policy gradient convergence. The reward function utilizes the reward shaping method to formulate cooperative reward components that explicitly capture behavioral correlations among agents. Finally, simulations show that the proposed method significantly outperforms multi-agent reinforcement learning baselines, effectively improving the performance of cooperative game guidance under complex constraints.
Liu et al. (Tue,) studied this question.