Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2023

Abstract

We address the problem of coordinating multiple agents in a dynamic police patrol scheduling via a Reinforcement Learning (RL) approach. Our approach utilizes Multi-Agent Value Function Approximation (MAVFA) with a rescheduling heuristic to learn dispatching and rescheduling policies jointly. Often, police operations are divided into multiple sectors for more effective and efficient operations. In a dynamic setting, incidents occur throughout the day across different sectors, disrupting initially-planned patrol schedules. To maximize policing effectiveness, police agents from different sectors cooperate by sending reinforcements to support one another in their incident response and even routine patrol. This poses an interesting research challenge on how to make such complex decision of dispatching and rescheduling involving multiple agents in a coordinated fashion within an operationally reasonable time. Unlike existing Multi-Agent RL (MARL) approaches which solve similar problems by either decomposing the problem or action into multiple components, our approach learns the dispatching and rescheduling policies jointly without any decomposition step. In addition, instead of directly searching over the joint action space, we incorporate an iterative best response procedure as a decentralized optimization heuristic and an explicit coordination mechanism for a scalable and coordinated decision-making. We evaluate our approach against the commonly adopted two-stage approach and conduct a series of ablation studies to ascertain the effectiveness of our proposed learning and coordination mechanisms.

Keywords

Agent-based and Multi-agent Systems, Multi-agent learning Planning and Scheduling, Learning in planning and scheduling, police agents

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023: Macao, August 19-25

First Page

153

Last Page

161

ISBN

9781956792034

Identifier

10.24963/ijcai.2023/18

Publisher

AAAI Press

City or Country

Washington, DC

Additional URL

https://doi.org/10.24963/ijcai.2023/18

Download

Share

COinS