Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation. Prior works on GSG-I have used deep reinforcement learning (DRL) to learn the best policy for the agent in such an environment without any need to store the huge number of state representations for GSG-I. However, the decision-making process of DRL methods is largely opaque, which results in a lack of trust in their predictions. To tackle this issue, we present an interpretable DRL method for GSG-I that generates visualization to explain the decisions taken by the DRL algorithm. We also show that this approach performs better and works well with a simpler training regimen compared to the existing method.

Motivated by the urgent need in green security domains such as protecting endangered wildlife from poaching and preventing illegal logging, researchers have proposed game theoretic models to optimize patrols conducted by law enforcement agencies. Despite the efforts, online information and online interactions (e.g., patrollers chasing the poachers by following their footprints) have been neglected in previous game models and solutions. Our research aims at providing a more practical solution for the complex real-world green security problems by empowering security games with deep reinforcement learning. Specifically, we propose a novel game model which incorporates the vital element of online information and provide a discussion of possible solutions as well as promising future research directions based on game theory and deep reinforcement learning.