Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation

ACL 2018

Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation

Jan 28, 2021
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Abstract: 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 in-teraction 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 re-ward 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. Authors: Alane Suhr, Yoav Artzi (Cornell University)

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