为强化学习提供统计推断工具，提升算法可信度与可解释性。
Statistical Inference in Reinforcement Learning: A Selective Survey
- 引入假设检验与置信区间方法，实现对强化学习策略的统计推断。
- 强调统计工具在医疗、网约车、大模型对齐等场景中的可靠性验证作用。
- 面向统计与机器学习交叉研究者，推动经典统计方法在RL中的应用。
强化学习（RL）关注智能体如何在特定环境中采取行动以最大化累积奖励。在医疗领域，应用RL算法可帮助患者改善健康状况；在共享出行平台，可提升司机收入与用户满意度；在大语言模型中，可使输出更符合人类偏好。过去十年间，强化学习无疑是机器学习领域最活跃的研究前沿之一。然而，与计算机科学相比，统计学界近期才开始深入、广泛地参与强化学习研究。本文综述了强化学习中的统计推断工具，涵盖假设检验与置信区间构建。目标是凸显统计推断在强化学习中的价值，促进统计与机器学习社区之间的交流，并推动经典统计推断方法在这一活跃研究领域的更广泛应用。
原文摘要 · Abstract (English)
Reinforcement learning (RL) is concerned with how intelligence agents take actions in a given environment to maximize the cumulative reward they receive. In healthcare, applying RL algorithms could assist patients in improving their health status. In ride-sharing platforms, applying RL algorithms could increase drivers' income and customer satisfaction. For large language models, applying RL algorithms could align their outputs with human preferences. Over the past decade, RL has been arguably one of the most vibrant research frontiers in machine learning. Nevertheless, statistics as a field, as opposed to computer science, has only recently begun to engage with RL both in depth and in breadth. This chapter presents a selective review of statistical inferential tools for RL, covering both hypothesis testing and confidence interval construction. Our goal is to highlight the value of statistical inference in RL for both the statistics and machine learning communities, and to promote the broader application of classical statistical inference tools in this vibrant area of research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。