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

arXiv 2211.04987 ↗First posted 9 Nov 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 9 Nov 2022.

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16 of 28 references.

References · 16

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  12. 2019Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attributioncited by 50
4 more
  1. 2018Deep Reinforcement Learning for Green Security Games with Real-Time Informationcited by 19
  2. 2020Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?cited by 9
  3. 2021Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learningcited by 9
  4. 2020Designing Interpretable Approximations to Deep Reinforcement Learningcited by 6

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