Pursuit Evasion Game

Pursuit-evasion games model the strategic interactions between pursuers and evaders, focusing on optimizing capture or escape strategies within defined environments. Current research heavily utilizes reinforcement learning, particularly multi-agent reinforcement learning (MARL) and hierarchical reinforcement learning (HRL), often incorporating graph neural networks (GNNs) for efficient decision-making in complex scenarios, such as urban environments with obstacles and limited sensing. These advancements are improving the efficiency and robustness of autonomous systems in applications like drone navigation and robotics, particularly in addressing challenges like safety and uncertainty in multi-agent coordination. The development of generalizable and safe pursuit-evasion strategies has significant implications for various fields, including robotics, autonomous systems, and game theory.