As a special type of dynamic game, Pursuit-Evasion Games (PEGs) have expanded their application range from initial military confrontations to areas such as navigation control and aerospace, demonstrating broad applicability and significant value in addressing a wide array of modern complex decision-making problems. Traditional optimal control methods based on differential game theory are classic approaches to solve PEG problems. However, these methods often struggle to perform well in complex environments, nonlinear systems, and situations involving highly uncertain participant behaviors. In recent years, rapidly developing Reinforcement Learning (RL) techniques has provided new avenues for PEG research. RL is capable of adapting to environmental changes through efficient online computation and feedback-driven learning, exhibiting strong generalization capabilities. Therefore, this survey presents a detailed and systematic review of PEG research based on RL methods. First, it classifies and discusses key RL algorithms and theoretical foundations in PEGs according to different forms of strategy learning. Then, it summarizes typical application scenarios, including tactical combat, unmanned systems control, and spacecraft interception, demonstrating the potential and effectiveness of RL in addressing real-world challenges. Finally, the survey explores current challenges and future opportunities in applying RL to PEGs, with the aim of promoting further research on more effective and practical solutions.

As an emerging delivery style in logistics, the cooperation between trucks and drones can significantly improve the efficiency of parcel delivery, especially in some typical scenes, such as mountainous areas, high buildings, or post-disaster material delivery. In recent years, the truck and drone cooperative delivery problem (TDCDP) has attracted more and more attention from logistic research and commercial sectors. This paper proposes a taxonomy for TDCDP and systematically summarizes the related research. First, the impacts of changes in customers and environments on truck and drone delivery modes are analyzed in detail. Second, by using the proposed taxonomy, the delivery modes in TDCDP are classified into four types: parallel delivery, mixed delivery, drone delivery with truck-assisting, and truck delivery with drone-assisting. The roles of trucks and drones are analyzed in different scenes. Then, for different delivery modes, this paper summarizes the TDCDP models and analyzes the common assumptions, constraints, and objective functions. This paper also combs the exact algorithms, heuristic algorithms, and hybrid algorithms used to solve different kinds of TDCDP. Finally, the current research status and future research trends are discussed, and the challenges of TDCDP are highlighted.