用相对熵逆强化学习推断投资者偏好，无需已知市场转移概率。
Model-Free Inference of Investor Preferences: A Relative Entropy IRL Approach
- 基于相对熵逆强化学习，从投资行为中还原收益函数。
- 采用K近邻估计策略，解决数据稀疏问题。
- 设计统计检验框架，验证结果有效性与鲁棒性。
我们提出一种基于相对熵逆强化学习（RE-IRL）的框架，从观测到的投资行为和市场条件中恢复投资者的收益函数。与传统逆强化学习算法不同，RE-IRL适用于转移概率未知或不可访问的环境。为应对数据稀疏问题，我们采用K近邻方法估计观测行为策略。此外，我们提出了一个统计检验框架，用于评估所估计结果的有效性和鲁棒性。
原文摘要 · Abstract (English)
We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment actions and market conditions. Unlike traditional IRL algorithms, RE-IRL is employed to account for environments where transition probabilities are unknown or inaccessible. To address the challenge of data sparsity, we utilize a $K$-nearest neighbor approach to estimate the observed behavior policy. Furthermore, we propose a statistical testing framework to evaluate the validity and robustness of the estimated results.
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