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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