Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement
  Learning
Evan Zheran Liu Sahaana Suri Tong Mu Allan Zhou Chelsea Finn
Whereas machine learning models typically learn language by directly training
on language tasks (e.g., next-word prediction), language emerges in human
children as a byproduct of solving non-language tasks (e.g., acquiring food).
Motivated by this observation, we ask: can embodied reinforcement learning (RL)
agents also indirectly learn language from non-language tasks? Learning to
associate language with its meaning requires a dynamic environment with varied
language. Therefore, we investigate this question in a multi-task environment
with language that varies across the different tasks. Specifically, we design
an office navigation environment, where the agent's goal is to find a
particular office, and office locations differ in different buildings (i.e.,
tasks). Each building includes a floor plan with a simple language description
of the goal office's location, which can be visually read as an RGB image when
visited. We find RL agents indeed are able to indirectly learn language. Agents
trained with current meta-RL algorithms successfully generalize to reading
floor plans with held-out layouts and language phrases, and quickly navigate to
the correct office, despite receiving no direct language supervision.