  Reinforcement Learning (RL) algorithms have been successfully applied to real
world situations like illegal smuggling, poaching, deforestation, climate
change, airport security, etc. These scenarios can be framed as Stackelberg
security games (SSGs) where defenders and attackers compete to control target
resources. The algorithm's competency is assessed by which agent is controlling
the targets. This review investigates modeling of SSGs in RL with a focus on
possible improvements of target representations in RL algorithms.
