  State abstraction enables sample-efficient learning and better task transfer
in complex reinforcement learning environments. Recently, we proposed RePReL
(Kokel et al. 2021), a hierarchical framework that leverages a relational
planner to provide useful state abstractions for learning. We present a brief
overview of this framework and the use of a dynamic probabilistic logic model
to design these state abstractions. Our experiments show that RePReL not only
achieves better performance and efficient learning on the task at hand but also
demonstrates better generalization to unseen tasks.
