Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning
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.
Cite
@article{arxiv.2209.12758,
title = {Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning},
author = {Hugo Caselles-Dupré and Olivier Sigaud and Mohamed Chetouani},
journal= {arXiv preprint arXiv:2209.12758},
year = {2023}
}
Comments
NeurIPS 2022 Workshop