Can world knowledge learned by large language models (LLMs) be used to act in
interactive environments? In this paper, we investigate the possibility of
grounding high-level tasks, expressed in natural language (e.g. "make
breakfast"), to a chosen set of actionable steps (e.g. "open fridge"). While
prior work focused on learning from explicit step-by-step examples of how to
act, we surprisingly find that if pre-trained LMs are large enough and prompted
appropriately, they can effectively decompose high-level tasks into mid-level
plans without any further training. However, the plans produced naively by LLMs
often cannot map precisely to admissible actions. We propose a procedure that
conditions on existing demonstrations and semantically translates the plans to
admissible actions. Our evaluation in the recent VirtualHome environment shows
that the resulting method substantially improves executability over the LLM
baseline. The conducted human evaluation reveals a trade-off between
executability and correctness but shows a promising sign towards extracting
actionable knowledge from language models. Website at
this https URL

本文研究大型语言模型在互动环境中是否可以利用所学的世界知识来执行高层任务，并提出了一种条件方法，将语言模型生成的中级计划语义上翻译为合适的操作以提高执行性能。在 VirtualHome 环境中的实证评估结果表明，该方法在可执行性方面显著优于大型语言模型基线。

语言模型作为零 - shot 规划器：提取行动知识用于具身代理

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Text-based games are a natural challenge domain for deep reinforcement
learning algorithms. Their state and action spaces are combinatorially large,
their reward function is sparse, and they are partially observable: the agent
is informed of the consequences of its actions through textual feedback. In
this paper we emphasize this latter point and consider the design of a deep
reinforcement learning agent that can play from feedback alone. Our design
recognizes and takes advantage of the structural characteristics of text-based
games. We first propose a contextualisation mechanism, based on accumulated
reward, which simplifies the learning problem and mitigates partial
observability. We then study different methods that rely on the notion that
most actions are ineffectual in any given situation, following Zahavy et al.'s
idea of an admissible action. We evaluate these techniques in a series of
text-based games of increasing difficulty based on the TextWorld framework, as
well as the iconic game Zork. Empirically, we find that these techniques
improve the performance of a baseline deep reinforcement learning agent applied
to text-based games.

本文研究利用深度强化学习算法可玩具有组合性、稀疏奖励、有部分可观察性的文本游戏。提出了基于累积奖励的上下文化机制，缓解了部分可观测性，研究了可行动作的不同方法，并在一系列不同难度的文本游戏和 Zork 游戏中进行了实证研究，结果表明这些技术提高了基于文本游戏的深度强化学习代理的性能。