Author

Edoardo Zorzi

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Preprint Jul 2026

Explaining Reinforcement Learning Agents via Inductive Logic Programming

This work employs Inductive Logic Programming (ILP) to extract symbolic representations of RL policies and define a novel set of explainability metrics, including activation rate, feature coverage, syntactic distance and semantic distance, which provide crucial insights for the transfer and generalization of action-specific policies.

Celeste Veronese, Edoardo Zorzi, Daniele Meli et al. · 0 citations