Policy Gradient in Partially Observable Environments: Approximation and Convergence
This work addresses the challenge of applying policy gradient reinforcement learning in real-world applications that are often partially observable, representing an incremental extension of existing theory.
The authors generalized policy gradient methods from fully observable to partially observable environments, proposing new technical tools and extending theoretical guarantees like convergence theorems to these settings.
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.