Complex, long-horizon planning and its combinatorial nature pose steep
challenges for learning-based agents. Difficulties in such settings are
exacerbated in low data regimes where over-fitting stifles generalization and
compounding errors hurt accuracy. In this work, we explore the use of an often
unused source of auxiliary supervision: language. Inspired by recent advances
in transformer-based models, we train agents with an instruction prediction
loss that encourages learning temporally extended representations that operate
at a high level of abstraction. Concretely, we demonstrate that instruction
modeling significantly improves performance in planning environments when
training with a limited number of demonstrations on the BabyAI and Crafter
benchmarks. In further analysis we find that instruction modeling is most
important for tasks that require complex reasoning, while understandably
offering smaller gains in environments that require simple plans. More details
and code can be found at this https URL

本研究探讨在长时间跨度规划中，利用语言指令作为辅助监督的方法，使用 transformer-based 的模型，通过 instruction modeling 不断改善 agent 在 BabyAI 和 Crafter 等基准环境中的规划表现。

通过指令预测提高长期模仿能力

Improving Long-Horizon Imitation Through Instruction Prediction

Text-based games (TBGs) have become a popular proving ground for the
demonstration of learning-based agents that make decisions in quasi real-world
settings. The crux of the problem for a reinforcement learning agent in such
TBGs is identifying the objects in the world, and those objects' relations with
that world. While the recent use of text-based resources for increasing an
agent's knowledge and improving its generalization have shown promise, we posit
in this paper that there is much yet to be learned from visual representations
of these same worlds. Specifically, we propose to retrieve images that
represent specific instances of text observations from the world and train our
agents on such images. This improves the agent's overall understanding of the
game 'scene' and objects' relationships to the world around them, and the
variety of visual representations on offer allow the agent to generate a better
generalization of a relationship. We show that incorporating such images
improves the performance of agents in various TBG settings.

本文通过使用 TEXT-BASED GAMES，提出了一种新的学习方法，利用视觉图像与文本信息相结合，提高了强化学习智能体的性能及其对于世界中物体及其关系的理解，从而提高了其通用性。