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
@article{arxiv.2309.07974,
title = {A Data Source for Reasoning Embodied Agents},
author = {Jack Lanchantin and Sainbayar Sukhbaatar and Gabriel Synnaeve and Yuxuan Sun and Kavya Srinet and Arthur Szlam},
journal= {arXiv preprint arXiv:2309.07974},
year = {2023}
}