BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop.
Year: 2018
Type: preprint
Abstract: Allowing humans to interactively train artificial agents to understandlanguage instructions is desirable for both practical and scientific reasons,
but given the poor data efficiency of the current learning methods, this goal
may require substantial research efforts. Here, we introduce the BabyAI
research platform to support investigations towards including humans in the
loop for grounded language learning. The BabyAI platform comprises an
extensible suite of 19 levels of increasing difficulty. The levels gradually
lead the agent towards acquiring a combinatorially rich synthetic language
which is a proper subset of English. The platform also provides a heuristic
expert agent for the purpose of simulating a human teacher. We report baseline
results and estimate the amount of human involvement that would be required to
train a neural network-based agent on some of the BabyAI levels. We put forward
strong evidence that current deep learning methods are not yet sufficiently
sample efficient when it comes to learning a language with compositional
properties.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W2897513296