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

• URL: http://arxiv.org/abs/2308.13278v1
• Date: Fri, 25 Aug 2023 10:00:06 GMT
• Title: Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity
• Authors: Achkan Salehi and Stephane Doncieux
• Abstract summary: Quality-Diversity is a branch of optimization that is often applied to problems from the Reinforcement Learning and control domains. We propose a Large Language Model to augment the repertoire with natural language descriptions of trajectories. We also propose an LLM-based approach to evaluating the performance of such generative agents.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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