想搞懂马尔可夫决策过程控制器可解释性的可以看看这个。dtControl2+ε用ε容忍度换更小更易懂的决策树，比原先的压缩效果好很多。
dtControl2+ε扩展了dtControl2工具，针对马尔可夫决策过程（MDP）中的控制器表示问题。通过引入允许的精度损失参数ε≥0，该工具能生成更小的决策树，同时保证ε-最优性。相比现有最优方法，生成的决策树规模可减少数个数量级，从而提升人机理解释性。方法在保持核心控制逻辑的前提下，可控地省略细节。
dtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees
Over the past decade, decision trees have been used to represent controllers (a.k.a. policies) in an explainable way, with dtControl2 as a current state-of-the-art tool. However, for systems that are large or have many corner cases, even such representations tend to be too complex and not human-comprehensible. Unfortunately, reducing the size of the decision tree is not straightforward, as missing just a single crucial case might result in an incorrect controller. We tackle this issue in the setting of Markov decision processes, extending dtControl2 by "$\varepsilon$" functionality: Given an allowed imprecision $\varepsilon \geq 0$, we construct a smaller decision tree, distilling the essence of the controller, while still guaranteeing its $\varepsilon$-optimality. This enables us to provide tunably simpler explanations, omitting a controllable amount of detail. Our tool constructs decision trees that are orders of magnitude smaller than the state of the art.