用剪枝简化强化学习决策树，提升可解释性且不损失性能
Interpretable reinforcement learning with decision-tree pruning
- 设计结构化剪枝算子，动态优化决策树规则
- 在经典控制与MuJoCo任务中保持高奖励水平
- 适合需要可信智能决策的工业应用
强化学习策略难以解释，而可解释性是信任的前提。将训练好的策略转化为显式的决策树规则虽能提升透明度，但生成的规则常过于复杂，难被人理解。本文提出一种剪枝方法，在保留任务性能的同时简化规则结构，并使策略修改过程可审计。该方法定义了一组结构化和使用感知的剪枝操作符，通过重新执行策略评估候选修改对回报及可解释性代理指标的影响。实验在经典控制与MuJoCo基准上验证，剪枝过程显著提升可解释性，同时维持高绩效水平。
原文摘要 · Abstract (English)
Reinforcement learning policies are difficult to inspect, but interpreting them is a prerequisite for trustworthiness. Converting a trained policy into explicit decision-tree rules improves transparency and the resulting artifacts often remain too complex for human understanding. We present a pruning process that simplifies such rule-based policies while preserving task performance and making edits to the policy auditable. The process defines a small set of structural and usage-aware operators and evaluates candidate edits by re-executing the policy to measure return and interpretability proxies. This exposes an transformation process from complex to compact policy structures. We investigate this approach on classic control and MuJoCo benchmarks, where pruning traces reveal consistent interpretability improvements while maintaining high performance.
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