This dissertation investigates the value of deep
reinforcement learning (DRL) within an agent-based model (ABM) of a large
open-air venue. The intent is to reduce civilian casualties in an active
shooting incident (ASI). There has been a steady increase of ASIs in the United
States of America for over 20 years, and some of the most casualty-producing
events have been in open spaces and open-air venues. More research should be
conducted within the field to help discover policies that can mitigate the
threat of a shooter in extremis. This study uses the concept of dynamic
signage, controlled by a DRL policy, to guide civilians away from the threat
and toward a safe exit in the modeled environment. It was found that a
well-trained DRL policy can significantly reduce civilian casualties as
compared to baseline scenarios. Further, the DRL policy can assist decision makers
in determining how many signs to use in an environment and where to place them.
Finally, research using DRL in the ASI space can yield systems and policies
that will help reduce the impact of active shooters during an incident.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17124326 |
| Date | 20 December 2021 |
| Creators | Robert Eugen Bott (11791199) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/USING_REINFORCEMENT_LEARNING_FOR_ACTIVE_SHOOTER_MITIGATION/17124326 |
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