协同巡逻路径规划：通过多智能体强化学习优化城市犯罪监控
Cooperative Patrol Routing: Optimizing Urban Crime Surveillance through Multi-Agent Reinforcement Learning
AI总结：
针对城市巡逻路径优化问题，提出基于去中心化部分可观测马尔可夫决策过程的MARL模型，采用VDPPO算法，引入覆盖指数指标，在马拉加市测试实现了符合警方标准的高犯罪区域覆盖效果。
摘要：
巡逻策略的有效设计是一个困难且复杂的问题，在中大型区域中尤其如此。其目标是以协同方式为给定区域内的一组巡逻人员规划最优路径，以实现区域的最大覆盖范围，同时尽量减少巡逻人员的数量。本文提出了一种基于去中心化部分可观测马尔可夫决策过程的多智能体强化学习（MARL）模型，用于在以无向图表示的城市环境中规划不可预测的巡逻路径。该模型试图最大化一个目标函数，该函数用于表征给定时间范围内的环境特征。我们的模型已在马拉加市的三个中型城区进行了警察巡逻路径优化测试，测试目标是基于该市的实际犯罪数据，最大化对犯罪高发区域的监控覆盖范围。为解决这一问题，我们研究了多种MARL算法，其中价值分解近端策略优化（VDPPO）算法表现出最佳性能。我们还提出了一种新的指标——覆盖指数，用于评估模型生成路径的覆盖性能。该指标的灵感来源于预测准确率指数（PAI），PAI是犯罪学中常用于检测热点区域的指标。利用该指标，我们在多种场景下对模型进行了评估，这些场景修改了智能体（或巡逻人员）的数量、它们的起始位置以及它们在环境中可观测的信息水平。结果表明，我们的模型生成的协同路径对犯罪发生率最高的3%图节点实现了90%以上的覆盖，对20%的这类节点实现了65%的覆盖；3%和20%是警察资源分配的覆盖标准。
英文摘要：
The effective design of patrol strategies is a difficult and complex problem, especially in medium and large areas. The objective is to plan, in a coordinated manner, the optimal routes for a set of patrols in a given area, in order to achieve maximum coverage of the area, while also trying to minimize the number of patrols. In this paper, we propose a multi-agent reinforcement learning (MARL) model, based on a decentralized partially observable Markov decision process, to plan unpredictable patrol routes within an urban environment represented as an undirected graph. The model attempts to maximize a target function that characterizes the environment within a given time frame. Our model has been tested to optimize police patrol routes in three medium-sized districts of the city of Malaga. The aim was to maximize surveillance coverage of the most crime-prone areas, based on actual crime data in the city. To address this problem, several MARL algorithms have been studied, and among these the Value Decomposition Proximal Policy Optimization (VDPPO) algorithm exhibited the best performance. We also introduce a novel metric, the coverage index, for the evaluation of the coverage performance of the routes generated by our model. This metric is inspired by the predictive accuracy index (PAI), which is commonly used in criminology to detect hotspots. Using this metric, we have evaluated the model under various scenarios in which the number of agents (or patrols), their starting positions, and the level of information they can observe in the environment have been modified. Results show that the coordinated routes generated by our model achieve a coverage of more than $90\%$ of the $3\%$ of graph nodes with the highest crime incidence, and $65\%$ for $20\%$ of these nodes; $3\%$ and $20\%$ represent the coverage standards for police resource allocation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
