mlsanityData to 7 Oct 2026

Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop

arXiv 2404.10906 ↗First posted 16 Apr 2024cs.AI cs.HC cs.LGcited by 0

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Abstract (arXiv)

Embracing the pursuit of intrinsically explainable reinforcement learning raises crucial questions: what distinguishes explainability from interpretability? Should explainable and interpretable agents be developed outside of domains where transparency is imperative? What advantages do interpretable policies offer over neural networks? How can we rigorously define and measure interpretability in policies, without user studies? What reinforcement learning paradigms,are the most suited to develop interpretable agents? Can Markov Decision Processes integrate interpretable state representations? In addition to motivate an Interpretable RL community centered around the aforementioned questions, we propose the first venue dedicated to Interpretable RL: the InterpPol Workshop.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 38 references that are in the map. First posted on arXiv 16 Apr 2024.

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References · 31

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19 more
  1. 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 122
  2. 2020Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanationscited by 94
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  7. 2021Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systemscited by 36
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