Dynamic police patrol scheduling with multi-agent reinforcement learning
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...
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sg-smu-ink.sis_research-93502023-12-13T03:33:43Z Dynamic police patrol scheduling with multi-agent reinforcement learning WONG, Songhan JOE, Waldy LAU, Hoong Chuin 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. 2023-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8347 info:doi/10.1007/978-3-031-44505-7_38 https://ink.library.smu.edu.sg/context/sis_research/article/9350/viewcontent/Dynamic.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Dynamic dispatch and rescheduling Multi-Agent Police patrolling Proximal policy optimization Reinforcement learning Programming Languages and Compilers |
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Dynamic dispatch and rescheduling Multi-Agent Police patrolling Proximal policy optimization Reinforcement learning Programming Languages and Compilers WONG, Songhan JOE, Waldy LAU, Hoong Chuin Dynamic police patrol scheduling with multi-agent reinforcement learning |
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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. |
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text |
author |
WONG, Songhan JOE, Waldy LAU, Hoong Chuin |
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WONG, Songhan JOE, Waldy LAU, Hoong Chuin |
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WONG, Songhan |
title |
Dynamic police patrol scheduling with multi-agent reinforcement learning |
title_short |
Dynamic police patrol scheduling with multi-agent reinforcement learning |
title_full |
Dynamic police patrol scheduling with multi-agent reinforcement learning |
title_fullStr |
Dynamic police patrol scheduling with multi-agent reinforcement learning |
title_full_unstemmed |
Dynamic police patrol scheduling with multi-agent reinforcement learning |
title_sort |
dynamic police patrol scheduling with multi-agent reinforcement learning |
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Institutional Knowledge at Singapore Management University |
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2023 |
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https://ink.library.smu.edu.sg/sis_research/8347 https://ink.library.smu.edu.sg/context/sis_research/article/9350/viewcontent/Dynamic.pdf |
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