Despite success in many real-world tasks (e.g., robotics), reinforcement
learning (RL) agents still learn from tabula rasa when facing new and dynamic
scenarios. By contrast, humans can offload this burden through textual
descriptions. Although recent works have shown the benefits of instructive
texts in goal-conditioned RL, few have studied whether descriptive texts help
agents to generalize across dynamic environments. To promote research in this
direction, we introduce a new platform, BabyAI++, to generate various dynamic
environments along with corresponding descriptive texts. Moreover, we benchmark
several baselines inherited from the instruction following setting and develop
a novel approach towards visually-grounded language learning on our platform.
Extensive experiments show strong evidence that using descriptive texts
improves the generalization of RL agents across environments with varied
dynamics.

该研究针对强化学习中的新环境和动态环境，通过使用描述性文本探究使用教育性文本是否有助于代理在这些环境下的泛化，并通过 BabyAI++ 开发了一种基于视觉语言学习的新方法，实验结果表明，使用描述性文本可以提高 RL 代理在不同动态环境下的泛化能力。

BabyAI++: 初探基于实体的语言学习

BabyAI++: Towards Grounded-Language Learning beyond Memorization

We present a model of visually-grounded language learning based on stacked
gated recurrent neural networks which learns to predict visual features given
an image description in the form of a sequence of phonemes. The learning task
resembles that faced by human language learners who need to discover both
structure and meaning from noisy and ambiguous data across modalities. We show
that our model indeed learns to predict features of the visual context given
phonetically transcribed image descriptions, and show that it represents
linguistic information in a hierarchy of levels: lower layers in the stack are
comparatively more sensitive to form, whereas higher layers are more sensitive
to meaning.

基于堆叠门控循环神经网络的视觉语言学习模型，可以通过音素序列描述的图像描述，学习预测图像的视觉特征，并以层级结构表示语言信息。