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.

该研究探讨了机器学习模型是否能够通过非语言任务间接学习语言，设计了一个含有变化语言环境和目的地的综合任务环境，通过元强化学习算法，培养出能够正确理解渐新语言和 floor plans 的智能体。