# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 1 Introduction  
Agile satellites have potential applications in various fields, such as agricultural census [1], disaster relief [2], environmental monitoring [3], urban planning [4], mapping [5], and military reconnaissance. Due to the limited attitude maneuver ability of traditional earth-observation satellites (EOSs) and various constraints such as the visible time window, it is challenging for the mission planning system to schedule all tasks within a mission planning time range, and only one subset can be selected with its observation order planned [6]. Agile satellites possess three-axis maneuverability, which enhances their flexibility and efficiency in processing remote sensing missions. On the other hand, the larger solution space of Agile Earth-Observation Satellite (AEOS) mission planning compared with that of EOSs considerably amplifies the planning complexity [7]. The increasing number of remote sensing missions and the complexity of agile-satellite control create a challenge for agile-satellite mission planning [6, 7]. Consequently, a reliable and efficient mission planning system is of great importance to fully utilize the potent observation capabilities of agile satellites. Since the inception of the first comprehensive study on the AEOS scheduling problem proposed by [PERSON] et al. in 2002 [8], which proved to be NP-hard, the problem has experienced a significant surge in interest over the past two decades [7]. Initially, exact algorithms were employed to tackle this problem, focusing on small-scale instances without considering satellite maneuvering. These algorithms, such as dynamic programming [8], branch and bound [9], and tree search [10], provide an accurate solution for the given problem. Owing to the explosion of the solution space, exact methods are impractical for larger-scale scenarios. Researchers have explored various heuristic and meta-heuristic algorithm approaches to accelerate the scheduling process of large-scale AEOS scenarios, including tabu algorithms [11; 12], iterated local search algorithms [13], genetic algorithms (GA) [14; 15], and ant colony optimization algorithms [16]. One of the most popular heuristic algorithms for solving Combinatorial Optimization Problems (COPs), including transportation and scheduling problems, is the Large Neighborhood Search (LNS) [17]. The LNS framework minimizes problem objectives by iteratively removing and inserting solution segments using \"destroy\" and \"repair\" operators [18; 19]. The operators of LNS are fixed, resulting in reduced search efficiency for more complex and dynamic problems [20]. Additionally, when considering the implementation of large neighborhood-based heuristics, the computational time required to explore the large neighborhood becomes a primary trade-off [21]. The Adaptive Large Neighborhood Search (ALNS) algorithm [22; 23] improves upon the LNS algorithm by modifying the algorithmic framework, leading to enhanced efficiency and the ability to handle larger problem instances, thereby significantly reducing the search time for solutions. [PERSON] et al. [19] proposed applying ALNS to address the AEOS scheduling problem and emphasized the time-dependent transition time in terms of problem features. Additionally, various deep learning strategies have been proposed to enhance the search efficiency of ALNS approaches. [PERSON] et al. [24] proposed a deep neural network model with an attention mechanism for repairing processes. [PERSON] et al. [25] proposed combining machine learning and two-opt operators to learn a stochastic policy to improve the solutions sequentially. Nonetheless, the quality of the solutions provided by ALNS for large-scale problems is not guaranteed. The performance of ALNS operators is solely based on past experiences, and in practical scenarios, it may be necessary to preselect operators based on the specific problem characteristics [21]. These approaches mentioned above have demonstrated satisfactory performance in small-sized EOS instances [26].