Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2023

Abstract

Effective police patrol scheduling is essential in projecting police presence and ensuring readiness in responding to unexpected events in urban environments. However, scheduling patrols can be a challenging task as it requires balancing between two conflicting objectives namely projecting presence (proactive patrol) and incident response (reactive patrol). This task is made even more challenging with the fact that patrol schedules do not remain static as occurrences of dynamic incidents can disrupt the existing schedules. In this paper, we propose a solution to this problem using Multi-Agent Reinforcement Learning (MARL) to address the Dynamic Bi-objective Police Patrol Dispatching and Rescheduling Problem (DPRP). Our solution utilizes an Asynchronous Proximal Policy Optimization-based (APPO) actor-critic method that learns a policy to determine a set of prescribed dispatch rules to dynamically reschedule existing patrol plans. The proposed solution not only reduces computational time required for training, but also improves the solution quality in comparison to an existing RL-based approach that relies on heuristic solver.

Keywords

Dynamic dispatch and rescheduling, Multi-Agent, Police patrolling, Proximal policy optimization, Reinforcement learning

Discipline

Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

Proceedings of the 17th International Conference on Learning and Intelligent Optimization, Nice, France, 2023 June 4-8

First Page

567

Last Page

582

ISBN

9783031445040

Identifier

10.1007/978-3-031-44505-7_38

Publisher

Springer

City or Country

Berlin

Additional URL

https://doi.org/10.1007/978-3-031-44505-7_38

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