为强化学习策略提供局部可解释性，揭示其决策边界与敏感区域。
STACHE: Local Black-Box Explanations for Reinforcement Learning Policies
- 基于状态空间结构，精确搜索动作不变的鲁棒区域与最小反事实扰动。
- 在Gymnasium环境中验证，能捕捉策略从混乱到稳定演化的全过程。
- 适合需要调试安全关键系统或理解模型决策逻辑的研究者。
强化学习智能体在稀疏奖励或安全关键环境中常表现出意外行为，亟需可靠的调试与验证工具。本文提出STACHE，一种用于离散马尔可夫博弈中特定动作的局部黑盒解释框架。该方法生成由两部分组成的复合解释：（1）鲁棒区域——动作保持不变的状态邻域；（2）最小反事实，即改变该决策所需的最小状态扰动。通过利用因子化状态空间的结构，我们设计了一种精确的搜索算法，避免了代理模型的保真度缺陷。在Gymnasium环境上的实证验证表明，该框架不仅能解释策略动作，还能有效捕捉训练过程中策略逻辑的演化——从不稳定、随机的行为逐步演变为优化且鲁棒的策略，为理解智能体敏感性和决策边界提供了可操作的洞察。
原文摘要 · Abstract (English)
Reinforcement learning agents often behave unexpectedly in sparse-reward or safety-critical environments, creating a strong need for reliable debugging and verification tools. In this paper, we propose STACHE, a comprehensive framework for generating local, black-box explanations for an agent's specific action within discrete Markov games. Our method produces a Composite Explanation consisting of two complementary components: (1) a Robustness Region, the connected neighborhood of states where the agent's action remains invariant, and (2) Minimal Counterfactuals, the smallest state perturbations required to alter that decision. By exploiting the structure of factored state spaces, we introduce an exact, search-based algorithm that circumvents the fidelity gaps of surrogate models. Empirical validation on Gymnasium environments demonstrates that our framework not only explains policy actions, but also effectively captures the evolution of policy logic during training - from erratic, unstable behavior to optimized, robust strategies - providing actionable insights into agent sensitivity and decision boundaries.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。