# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 1 Introduction
However, in some actual applications, the aforementioned methods cannot be applied directly and effectively. In agile-satellite mission planning, attitude maneuvering introduces additional resources and time consumption, leading to increased constraints on satellite resources and planning time. Heuristic algorithms face challenges in optimizing resource utilization and solution search speed. As the scale of agile-satellite mission planning expands, heuristic and meta-heuristic methods struggle to balance efficiency and solution quality. Accordingly, the solution quality deteriorates significantly, and there is a risk of failing to find a feasible solution within the specified time constraints [19]. Another category of approaches for solving planning problems is known as DRL. Compared with heuristic algorithms, DRL greatly improves the search speed of optimal solutions and the ability to solve large-scale problems [27]. Recent research has demonstrated the effectiveness and excellent performance of DRL in solving agile-satellite mission planning problems: [PERSON] et al. [28] utilized the DRL framework in real-time scheduling of image satellites by maximizing a Dynamic and Stochastic Knapsack Problem and the total expected profit objective. [PERSON] et al. [29] proposed an end-to-end framework based on DRL, which views neural networks as complex heuristic methods constructed by observing reward signals and following feasible rules, and incorporates RNNs and attention mechanisms. [PERSON] et al. [30] used the deep deterministic policy gradient algorithm (DDPG) to solve the time-continuous satellite task-scheduling problem and employed graph clustering in the preprocessing phase. [PERSON] et al. [31] modeled the agile-satellite scheduling problem as a finite Markov decision process (MDP) and proposed a learning-based reinforcement method, which efficiently generates high profits for various scheduling problems using a Q-network guided by a trained value function. Despite some work being conducted for DRL-based mission planning, the results of DRL depend on the effectiveness of neural network model training, and the data utilization rates are relatively low [32]. Compared with the traditional heuristic algorithm, it is more facile to converge to a non-optimal solution, and the solution quality of these DRL methods requires further improvement. In this paper, we propose a satellite mission planning algorithm based on the dynamic destroy deep-reinforcement learning (D3 RL) model, which combines the DRL framework with the LNS algorithm to enhance adaptability and computation efficiency while maintaining the quality of the solution. Specifically, a GAT-based D3 RL model is designed to address the challenges of agile-satellite mission planning. First, based on the MDP, the Proximal Policy Optimization (PPO) algorithm [33] is used to train the D3 RL model. Later, we integrate the DRL strategy with the heuristic algorithm and apply the D3 RL model to the adaptive destruction process of the LNS. Furthermore, the DRL method, which extends the application scope of the D3 RL model, also demonstrates promising outcomes. The following is a summary of the main contributions:  
* A D3 RL model is designed for the AEOS scheduling problem, focusing on large-scale scenarios. The state, action, and reward function is clearly explained in MDP. * A GAT-based actor-network is established using the target clustering information. The clustering graph for the satellite mission is built using an adjacency matrix based on the constraints provided. * Two application methods for practical agile-satellite mission planning are proposed: the DRL method and the DRL-LNS method. The DRL method utilizes the D3 RL model for direct inference, while an improved dynamic destroy operator based on the D3 RL model is developed for the DRL-LNS. * Comparison experiments are implemented between our algorithms and other algorithms (GA, ALNS).