Recent work has described neural-network-based agents that are trained with
reinforcement learning (RL) to execute language-like commands in simulated
worlds, as a step towards an intelligent agent or robot that can be instructed
by human users. However, the optimisation of multi-goal motor policies via deep
RL from scratch requires many episodes of experience. Consequently,
instruction-following with deep RL typically involves language generated from
templates (by an environment simulator), which does not reflect the varied or
ambiguous expressions of real users. Here, we propose a conceptually simple
method for training instruction-following agents with deep RL that are robust
to natural human instructions. By applying our method with a state-of-the-art
pre-trained text-based language model (BERT), on tasks requiring agents to
identify and position everyday objects relative to other objects in a
naturalistic 3D simulated room, we demonstrate substantially-above-chance
zero-shot transfer from synthetic template commands to natural instructions
given by humans. Our approach is a general recipe for training any deep
RL-based system to interface with human users, and bridges the gap between two
research directions of notable recent success: agent-centric motor behavior and
text-based representation learning.

通过使用预先训练的文本语言模型（BERT），我们提出了一种简单的方法来训练使用深度 RL 的指令跟踪代理，以适应自然人指令，从而在人类给出自然指令的情况下，实现从合成模板命令到指令的零 - shot 传输。

通过从文本的迁移学习进行深度强化学习的人类指令遵循

Human Instruction-Following with Deep Reinforcement Learning via  Transfer-Learning from Text

Research in multi-agent cooperation has shown that artificial agents are able
to learn to play a simple referential game while developing a shared lexicon.
This lexicon is not easy to analyze, as it does not show many properties of a
natural language. In a simple referential game with two neural network-based
agents, we analyze the object-symbol mapping trying to understand what kind of
strategy was used to develop the emergent language. We see that, when the
environment is uniformly distributed, the agents rely on a random subset of
features to describe the objects. When we modify the objects making one feature
non-uniformly distributed,the agents realize it is less informative and start
to ignore it, and, surprisingly, they make a better use of the remaining
features. This interesting result suggests that more natural, less uniformly
distributed environments might aid in spurring the emergence of better-behaved
languages.

本文研究了多代理协作，发现当人工智能代理学习玩简单参考游戏的同时，也能够建立共享的词汇表。通过分析神经网络代理在简单的参考游戏中使用符号进行对象映射的策略，发现对于非均匀分布的环境，代理人将忽略无用特征并更好地利用其他特征进行通信，这为语言的形成提供了新的启示。