  Language Models and Vision Language Models have recently demonstrated
unprecedented capabilities in terms of understanding human intentions,
reasoning, scene understanding, and planning-like behaviour, in text form,
among many others. In this work, we investigate how to embed and leverage such
abilities in Reinforcement Learning (RL) agents. We design a framework that
uses language as the core reasoning tool, exploring how this enables an agent
to tackle a series of fundamental RL challenges, such as efficient exploration,
reusing experience data, scheduling skills, and learning from observations,
which traditionally require separate, vertically designed algorithms. We test
our method on a sparse-reward simulated robotic manipulation environment, where
a robot needs to stack a set of objects. We demonstrate substantial performance
improvements over baselines in exploration efficiency and ability to reuse data
from offline datasets, and illustrate how to reuse learned skills to solve
novel tasks or imitate videos of human experts.
