@inproceedings{
nam2023lift,
title={Li{FT}: Unsupervised Reinforcement Learning with Foundation Models as Teachers},
author={Taewook Nam and Juyong Lee and Jesse Zhang and Sung Ju Hwang and Joseph J Lim and Karl Pertsch},
booktitle={Second Agent Learning in Open-Endedness Workshop},
year={2023},
url={https://openreview.net/forum?id=sFF9UIh3iK}
}
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human intervention.
In our framework, the agent receives task instructions grounded in a training environment from large language models.
Then, a vision-language model guides the agent in learning the tasks by providing reward feedback.
We demonstrate that our method can learn semantically meaningful skills in a challenging open-ended MineDojo environment, while prior works on unsupervised skill discovery methods struggle.
Additionally, we discuss the observed challenges of using off-the-shelf foundation models as teachers and our efforts to address them.
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
Taewook Nam Juyong Lee Jesse Zhang Sung Ju Hwang Joseph J Lim Karl Pertsch