A Reinforcement Learning for Criminal’s Escape Path Prediction

Abstract

A real-time decision support system with the capability to provide information related to possible criminal’s escape path can be very useful for a law enforcement to pursue a perpetrator after a crime has been committed. Typically, the exact escape path is unknown, and pursuers must relied on a predicted path based on available information about the environment. In static environment, a perpetrator may escape through an optimal path that is predicted using any existing optimal path finding algorithms. However, the path can be dynamic when environment is changed. The perpetrator may decide to change path when there is information about foremost changes in environment. This paper models the perpetrator’s path selection as a Markov Decision Process (MDP) and apply Q-learning to solve for a perpetrator’s escape path. The experiment results shows that our algorithm can find most probable escape path in the dynamic environment, which can be significant reference in a real-time decision support system for law enforcement applications.