  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.
