Batch policy learning in average reward Markov decision processes
研究了无限时域马尔可夫决策过程中的批量策略学习问题，提出一种双稳健估计器来最大化长期平均奖励，并开发优化算法计算最优策略，通过模拟和移动健康研究验证了方法。
We consider the batch (off-line) policy learning problem in the infinite horizon Markov Decision Process. Motivated by mobile health applications, we focus on learning a policy that maximizes the long-term average reward. We propose a doubly robust estimator for the average reward and show that it achieves semiparametric efficiency. Further we develop an optimization algorithm to compute the optimal policy in a parameterized stochastic policy class. The performance of the estimated policy is measured by the difference between the optimal average reward in the policy class and the average reward of the estimated policy and we establish a finite-sample regret guarantee. The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.