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cs.LG2026
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
Ignacio D. Lopez-Miguel, Ezio Bartocci, Thomas Eiter +1
Explainability remains a key issue in reinforcement learning (RL). Distilling an interpretable policy from an agent trained in a complex environment is particularly challenging whe…
cs.LG2025
Rule-Guided Reinforcement Learning Policy Evaluation and Improvement
Martin Tappler, Ignacio D. Lopez-Miguel, Sebastian Tschiatschek +1
We consider the challenging problem of using domain knowledge to improve deep reinforcement learning policies. To this end, we propose LEGIBLE, a novel approach, following a multi-…