  Policy gradient is a generic and flexible reinforcement learning approach
that generally enjoys simplicity in analysis, implementation, and deployment.
In the last few decades, this approach has been extensively advanced for fully
observable environments. In this paper, we generalize a variety of these
advances to partially observable settings, and similar to the fully observable
case, we keep our focus on the class of Markovian policies. We propose a series
of technical tools, including a novel notion of advantage function, to develop
policy gradient algorithms and study their convergence properties in such
environments. Deploying these tools, we generalize a variety of existing
theoretical guarantees, such as policy gradient and convergence theorems, to
partially observable domains, those which also could be carried to more
settings of interest. This study also sheds light on the understanding of
policy gradient approaches in real-world applications which tend to be
partially observable.
