  Recent progress in using machine learning models for reasoning tasks has been
driven by novel model architectures, large-scale pre-training protocols, and
dedicated reasoning datasets for fine-tuning. In this work, to further pursue
these advances, we introduce a new data generator for machine reasoning that
integrates with an embodied agent. The generated data consists of templated
text queries and answers, matched with world-states encoded into a database.
The world-states are a result of both world dynamics and the actions of the
agent. We show the results of several baseline models on instantiations of
train sets. These include pre-trained language models fine-tuned on a
text-formatted representation of the database, and graph-structured
Transformers operating on a knowledge-graph representation of the database. We
find that these models can answer some questions about the world-state, but
struggle with others. These results hint at new research directions in
designing neural reasoning models and database representations. Code to
generate the data will be released at github.com/facebookresearch/neuralmemory

Machine Learning;Artificial Intelligence