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

Alane Suhr; Yoav Artzi

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arXiv · 1805.10209

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

Alane Suhr·Yoav Artzi

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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 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.

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Alane Suhr, Yoav Artzi. 2018-05-25. Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation. https://arxiv.org/abs/1805.10209

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