# Multi-Agent Reinforcement Learning for Joint Police Patrol and Dispatch

arXiv: https://arxiv.org/abs/2409.02246 · HTML: https://mlsanity.com/paper/2409.02246/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Fairness in Reinforcement Learning](https://mlsanity.com/paper/1611.03071/) (2016)
- [Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing](https://mlsanity.com/paper/2102.07475/) (2021)
- [Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing](https://mlsanity.com/paper/2010.12001/) (2020)
- [Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic](https://mlsanity.com/paper/2105.05701/) (2021)
- [DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning](https://mlsanity.com/paper/2109.08665/) (2021)
- [Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1710.00336/) (2017)

## Built on by (most cited first, 2 of 2)

- [A Comparative Simulation Study of the Fairness and Accuracy of Predictive Policing Systems in Baltimore City](https://mlsanity.com/paper/2602.02566/) (2026)
- [Traffic Adaptive Moving-window Service Patrolling for Real-time Incident Management during High-impact Events](https://mlsanity.com/paper/2504.11570/) (2025)

## Abstract (arXiv)

Police patrol units need to split their time between performing preventive patrol and being dispatched to serve emergency incidents. In the existing literature, patrol and dispatch decisions are often studied separately. We consider joint optimization of these two decisions to improve police operations efficiency and reduce response time to emergency calls. Methodology/results: We propose a novel method for jointly optimizing multi-agent patrol and dispatch to learn policies yielding rapid response times. Our method treats each patroller as an independent Q-learner (agent) with a shared deep Q-network that represents the state-action values. The dispatching decisions are chosen using mixed-integer programming and value function approximation from combinatorial action spaces. We demonstrate that this heterogeneous multi-agent reinforcement learning approach is capable of learning joint policies that outperform those optimized for patrol or dispatch alone. Managerial Implications: Policies jointly optimized for patrol and dispatch can lead to more effective service while targeting demonstrably flexible objectives, such as those encouraging efficiency and equity in response.