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Quality-Diversity is a branch of stochastic optimization that is oftenapplied to problems from the Reinforcement Learning and control domains inorder to construct repertoires of well-performing policies/skills that exhibitdiversity with respect to a behavior space. Such archives are usually composedof a finite number of reactive agents which are each associated to a uniquebehavior descriptor, and instantiating behavior descriptors outside of thatcoarsely discretized space is not straight-forward. While a few recent workssuggest solutions to that issue, the trajectory that is generated is not easilycustomizable beyond the specification of a target behavior descriptor. Wepropose to jointly solve those problems in environments where semanticinformation about static scene elements is available by leveraging a LargeLanguage Model to augment the repertoire with natural language descriptions oftrajectories, and training a policy conditioned on those descriptions. Thus,our method allows a user to not only specify an arbitrary target behaviordescriptor, but also provide the model with a high-level textual prompt toshape the generated trajectory. We also propose an LLM-based approach toevaluating the performance of such generative agents. Furthermore, we develop abenchmark based on simulated robot navigation in a 2d maze that we use forexperimental validation.

Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity