LiFT: Unsupervised Reinforcement Learning with Foundation Models as
  Teachers
Taewook Nam Juyong Lee Jesse Zhang Sung Ju Hwang Joseph J. Lim Karl Pertsch
We propose a framework that leverages foundation models as teachers, guiding
a reinforcement learning agent to acquire semantically meaningful behavior
without human feedback. 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 multi-task
language-conditioned policy by providing reward feedback. We demonstrate that
our method can learn semantically meaningful skills in a challenging open-ended
MineDojo environment while prior unsupervised skill discovery methods struggle.
Additionally, we discuss observed challenges of using off-the-shelf foundation
models as teachers and our efforts to address them.