Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning. In this paper, we propose a simple but effective neural language grounding module for embodied agents that can be trained end to end from scratch taking raw pixels, unstructured linguistic commands, and sparse rewards as the inputs. We model the language grounding process as a language-guided transformation of visual features, where latent sentence embeddings are used as the transformation matrices. In several language-directed navigation tasks that feature challenging partial observation and require simple reasoning, our module significantly outperforms the state of the arts. We also release XWORLD 3D, an easy-to-customize 3D environment that can potentially be modified to evaluate a variety of embodied agents.

本文提出了一种简单但有效的神经语言基础模块，可以使用深度强化学习训练具有身体实体的代理，以执行语言引导的任务，在许多具有挑战性的部分可观察性和简单推理要求的语言引导导航任务中，我们的模块显著优于现有技术；我们还发布了一个易于定制的3D环境XWorld3D，可以潜在地修改以评估各种具体化代理。

引导特征转换（GFT）：面向具身代理的神经语言基础模块