  Beyond sharing datasets or simulations, we believe the Recommender Systems
(RS) community should share Task Environments. In this work, we propose a
high-level logical architecture that will help to reason about the core
components of a RS Task Environment, identify the differences between
Environments, datasets and simulations; and most importantly, understand what
needs to be shared about Environments to achieve reproducible experiments. The
work presents itself as valuable initial groundwork, open to discussion and
extensions.
