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arXiv · 2209.12758

Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning

Hugo Caselles-Dupré·Olivier Sigaud·Mohamed Chetouani
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Abstract

Teaching an agent to perform new tasks using natural language can easily be hindered by ambiguities in interpretation. When a teacher provides an instruction to a learner about an object by referring to its features, the learner can misunderstand the teacher's intentions, for instance if the instruction ambiguously refer to features of the object, a phenomenon called referential ambiguity. We study how two concepts derived from cognitive sciences can help resolve those referential ambiguities: pedagogy (selecting the right instructions) and pragmatism (learning the preferences of the other agents using inductive reasoning). We apply those ideas to a teacher/learner setup with two artificial agents on a simulated robotic task (block-stacking). We show that these concepts improve sample efficiency for training the learner.

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Hugo Caselles-Dupré, Olivier Sigaud, Mohamed Chetouani. 2022-09-26. Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning. https://arxiv.org/abs/2209.12758

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