Machine learningDeep learning / NLP / CV
可解释强化学习
可解释强化学习（Explainable Reinforcement Learning, XRL）通过使强化学习智能体的策略、决策和学习行为对人类可解释的方法，增强了标准强化学习智能体。XRL并非将策略视为黑箱，而是生成事后解释或构建本质上透明的策略，从而在高风险自动化决策中实现信任验证、调试和问责。
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来源
- 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. link ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Reinforcement Learning (XRL). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-reinforcement-learning
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