Interpretable reinforcement learning with decision-tree pruning
Quick summary
arXiv:2608.07151v1 Announce Type: cross Abstract: 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 p
Key takeaways
- arXiv:2608.07151v1 Announce Type: cross Abstract: 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.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “Interpretable reinforcement learning with decision-tree pruning” may reshape data collection, model training, output accountability and market access.
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