论文标题
带有实时信息的绿色安全游戏的可解释的深入增强学习
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information
论文作者
论文摘要
带有实时信息（GSG-I）的绿色安全游戏将有关代理机芯的实时信息添加到典型的GSG公式中。关于GSG-I的先前工作已经使用了深入的强化学习（DRL）来在这种环境中学习代理商的最佳政策，而无需为GSG-I存储大量的州代表。但是，DRL方法的决策过程在很大程度上是不透明的，这导致对他们的预测缺乏信任。为了解决此问题，我们提出了一种可解释的GSG-I的DRL方法，该方法生成可视化以解释DRL算法做出的决定。我们还表明，与现有方法相比，这种方法的性能更好，并且可以很好地与更简单的培训方案合作。
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.
