Sequential Knockoffs for Variable Selection in Reinforcement Learning
强化学习变量选择的序贯 Knockoffs 方法AI 解读 ↓
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强化学习变量选择的序贯 Knockoffs 方法
- 研究问题
- 在缺乏先验知识、状态变量过多的强化学习中，如何选择同时保留马尔可夫性质与原奖励函数的最小充分状态？
- 方法与思路
- 作者定义最小充分状态，并提出序贯 knockoffs 算法 SEEK，在高维复杂非线性动态系统中估计所需状态子向量。
- 材料陈述的贡献
- 摘要称该方法具有大样本选择一致性，可配合不同强化学习算法；实验报告了变量选择准确性和策略次优差距上的优势。
- 阅读关注点
- 应核对最小充分状态的存在性与唯一性、序贯 knockoffs 的构造条件，以及选择一致性和下游策略表现各自需要的假设。
- 阅读边界
- 仅据所给 OpenAlex 版本摘要；未说明动态依赖、变量分布及样本量条件，不能据实验概括普遍优势，也不能推断后续刊本变化。
原文摘要
In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge. Consequently, it is common practice to construct a state larger than necessary, e.g., by concatenating measurements over contiguous time points. However, needlessly increasing the dimension of the state may slow learning and obfuscate the learned policy. We introduce the notion of a minimal sufficient state in a Markov decision process (MDP) as the subvector of the original state under which the process remains an MDP and shares the same reward function as the original process. We propose a novel sequential knockoffs (SEEK) algorithm that estimates the minimal sufficient state in a system with high-dimensional complex nonlinear dynamics. In large samples, the proposed method achieves selection consistency. As the method is agnostic to the reinforcement learning algorithm being applied, it benefits downstream tasks such as policy learning. Empirical experiments verify theoretical results and show that the proposed approach outperforms several competing methods regarding variable selection accuracy and the suboptimality gap of the learned policy. A Python implementation of SEEK is provided at https://github.com/Mamba413/seek.