  We consider a learning agent in a partially observable environment, with
which the agent has never interacted before, and about which it learns both
what it can observe and how its actions affect the environment. The agent can
learn about this domain from experience gathered by taking actions in the
domain and observing their results. We present learning algorithms capable of
learning as much as possible (in a well-defined sense) both about what is
directly observable and about what actions do in the domain, given the
learner's observational constraints. We differentiate the level of domain
knowledge attained by each algorithm, and characterize the type of observations
required to reach it. The algorithms use dynamic epistemic logic (DEL) to
represent the learned domain information symbolically. Our work continues that
of Bolander and Gierasimczuk (2015), which developed DEL-based learning
algorithms based to learn domain information in fully observable domains.
