Quality-Diversity is a branch of stochastic optimization that is often
applied to problems from the Reinforcement Learning and control domains in
order to construct repertoires of well-performing policies/skills that exhibit
diversity with respect to a behavior space. Such archives are usually composed
of a finite number of reactive agents which are each associated to a unique
behavior descriptor, and instantiating behavior descriptors outside of that
coarsely discretized space is not straight-forward. While a few recent works
suggest solutions to that issue, the trajectory that is generated is not easily
customizable beyond the specification of a target behavior descriptor. We
propose to jointly solve those problems in environments where semantic
information about static scene elements is available by leveraging a Large
Language Model to augment the repertoire with natural language descriptions of
trajectories, and training a policy conditioned on those descriptions. Thus,
our method allows a user to not only specify an arbitrary target behavior
descriptor, but also provide the model with a high-level textual prompt to
shape the generated trajectory. We also propose an LLM-based approach to
evaluating the performance of such generative agents. Furthermore, we develop a
benchmark based on simulated robot navigation in a 2d maze that we use for
experimental validation.

通过使用大型语言模型扩充轨迹的自然语言描述，我们提出了一种方法来解决质量 - 多样性问题，允许用户指定任意目标行为描述，并通过高层次的文本提示来塑造生成的轨迹。同时，我们还提出了一种基于大型语言模型的评估方法，并使用 2D 迷宫中的模拟机器人导航开发了一种基准测试用于实验验证。