Interpretable reinforcement learning with decision-tree pruning
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 investig...
| Authors | Mark Leon Ringer, Michel Tokic |
|---|---|
| Primary category | cs.LG |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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