Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation
Alane Suhr, Yoav Artzi · 2018
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 singlestep 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.