We propose a learning approach for mapping context-dependent sequential instructions to actions. We address the problem of discourse and state dependencies with an attention-based model that considers both the history of the interaction and the state of the world. To train from start and goal states without access to demonstrations, we propose SESTRA, a learning algorithm that takes advantage of single-step reward observations and immediate expected reward maximization. We evaluate on the SCONE domains, and show absolute accuracy improvements of 9.8%-25.3% across the domains over approaches that use high-level logical representations.
@article{arxiv.1805.10209,
title = {Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation},
author = {Alane Suhr and Yoav Artzi},
journal= {arXiv preprint arXiv:1805.10209},
year = {2018}
}