学习型角落选择启发式改进 CADENCE 算法效率
On Learning Optimal Corners in Orthogonal Partially Observable Cooperative Guard Art Galleries
CADENCE 算法部署守卫时不知道该把智能体放到哪个角落，这篇论文用 CNN 和 GATv2+DQN 学会挑角落，7500 次测试跑得更快、用的智能体更少，形式化保证还一点没丢。
针对部分可观测协作守卫艺术画廊问题（POCGAGP），研究者提出两种保持 CADENCE 形式化保证的角落选择启发式：一个基于网格编码的 CNN 打分器，和一个用 Deep Q-Learning（DQN）在可见性图上训练的 GATv2 网络。在 7,500 次随机正交环境（50x50 到 250x250）测试中，两种启发式在完全覆盖所需步数和峰值智能体数量上均优于基线 CADENCE，且规模越大优势越明显。相比 Incremental Self-Deployment（ISDA）基线，新方法在智能体利用率上更高，同时保留了 ISDA 缺乏的形式化保证。
On Learning Optimal Corners in Orthogonal Partially Observable Cooperative Guard Art Galleries
The CADENCE algorithm solves the Partially Observable Cooperative Guard Art Gallery Problem (POCGAGP) with formal coverage and connectivity guarantees, but leaves unspecified which valid corner each agent should be deployed to, a choice that strongly affects efficiency. We introduce two learned corner-selection heuristics that preserve these guarantees: a CNN scoring candidates on a grid encoding, and a GATv2 network trained with Deep Q-Learning (DQN) on a visibility graph. Across 7,500 runs on random orthogonal environments (50x50 to 250x250), our heuristics outperform baseline CADENCE in both steps to full coverage and peak agent count, with gains growing with scale, and improve on Incremental Self-Deployment (ISDA) baselines in agent utilization while providing guarantees ISDA lacks. Learned corner selection thus improves CADENCE in speed and agent utilization at no cost to its formal properties.
