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<p>This paper presents a systematic review of explainable reinforcement learning methodologies with an emphasis on human-centered evaluation frameworks. Drawing from literature between 2017 and 2025, we apply and extend the Reasons, Confidence, and Counterfactuals (RCC) framework—originally designed for supervised learning—to reinforcement learning contexts. Our analysis reveals two predominant explanatory strategies: constructive, where explicit explanations are generated, and supportive, where users must infer reasoning from provided visual or textual cues. Our review also emphasizes human factor considerations, like task complexity, explanation formats, and evaluation methodologies. Particularly, for the latter, our analysis shows that improvement of the quality of decision is rarely measured.</p>

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A systematic review of human-centered explainability in reinforcement learning: transferring the RCC framework to support epistemic trustworthiness

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月2日

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  • Maximilian Moll,
  • John Dorsch

摘要

This paper presents a systematic review of explainable reinforcement learning methodologies with an emphasis on human-centered evaluation frameworks. Drawing from literature between 2017 and 2025, we apply and extend the Reasons, Confidence, and Counterfactuals (RCC) framework—originally designed for supervised learning—to reinforcement learning contexts. Our analysis reveals two predominant explanatory strategies: constructive, where explicit explanations are generated, and supportive, where users must infer reasoning from provided visual or textual cues. Our review also emphasizes human factor considerations, like task complexity, explanation formats, and evaluation methodologies. Particularly, for the latter, our analysis shows that improvement of the quality of decision is rarely measured.