Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
研究了存在未测量混杂因素时连续动作空间的离线策略学习问题，提出非参数估计方法估计策略价值，并开发基于策略梯度的算法寻找最优策略，通过模拟和德国家庭面板数据验证有效性。
This paper addresses the challenge of offline policy learning in continuous action spaces when unmeasured confounders are present. While most existing research focuses on policy evaluation within partially observable Markov decision processes (POMDPs) and assumes discrete action spaces, we advance this field by establishing a novel identification result to enable the nonparametric estimation of policy value for a given target policy under an infinite-horizon framework. Leveraging this identification, we develop a minimax estimator and introduce a policy-gradient-based algorithm to identify the in-class optimal policy that maximizes the estimated policy value. Furthermore, we provide theoretical results regarding the consistency, finite-sample error bound, and regret bound of the resulting optimal policy. Extensive simulations and a real-world application using the German Family Panel data demonstrate the effectiveness of our proposed methodology.