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

Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information