方法证据记录
Explainable Reinforcement Learning
Explainable Reinforcement Learning (XRL) augments standard reinforcement learning agents with methods that make their policies, decisions, and learned behaviors interpretable to humans. Rather than treating the policy as a black box, XRL produces post-hoc explanations or builds inherently transparent policies, enabling trust verification, debugging, and accountability in high-stakes automated decision-making.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Explainable Reinforcement Learning (XRL)
分类方法记录 · ml-model / deep-learning
- Puiutta, E., & Veith, E. M. S. P. (2020). Explainable Reinforcement Learning: A Survey. In Machine Learning and Knowledge Extraction (CD-MAKE 2020), Lecture Notes in Computer Science, vol. 12279, pp. 77–95. Springer. · DOI 10.1007/978-3-030-57321-8_5
- Explainable artificial intelligence. Wikipedia. · URL
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