Fast and the Furious: Hot Starts in Pursuit-Evasion Games
This work addresses a challenge in robotics or security applications for positioning agents in pursuit-evasion games, but it is incremental as it builds on existing game-theoretic and neural network methods.
The paper tackled the problem of positioning pursuers in pursuit-evasion games without prior knowledge of evader locations by introducing a method combining game-theoretic control theory with Graph Neural Networks to generate strategic initial configurations called 'hot starts'. The result showed that these hot starts provided a significant advantage over random configurations, hastening evader decline, reducing travel distances, and enhancing containment in multi-pursuer-evader scenarios.
Effectively positioning pursuers in pursuit-evasion games without prior knowledge of evader locations remains a significant challenge. A novel approach that combines game-theoretic control theory with Graph Neural Networks is introduced in this work. By conceptualizing pursuer configurations as strategic arrangements and representing them as graphs, a Graph Characteristic Space is constructed via multi-objective optimization to identify Pareto-optimal configurations. A Graph Convolutional Network (GCN) is trained on these Pareto-optimal graphs to generate strategically effective initial configurations, termed "hot starts". Empirical evaluations demonstrate that the GCN-generated hot starts provide a significant advantage over random configurations. In scenarios considering multiple pursuers and evaders, this method hastens the decline in evader survival rates, reduces pursuer travel distances, and enhances containment, showcasing clear strategic benefits.