  Reinforcement learning (RL) agents typically learn tabula rasa, without prior
knowledge of the world. However, if initialized with knowledge of high-level
subgoals and transitions between subgoals, RL agents could utilize this
Abstract World Model (AWM) for planning and exploration. We propose using
few-shot large language models (LLMs) to hypothesize an AWM, that will be
verified through world experience, to improve sample efficiency of RL agents.
Our DECKARD agent applies LLM-guided exploration to item crafting in Minecraft
in two phases: (1) the Dream phase where the agent uses an LLM to decompose a
task into a sequence of subgoals, the hypothesized AWM; and (2) the Wake phase
where the agent learns a modular policy for each subgoal and verifies or
corrects the hypothesized AWM. Our method of hypothesizing an AWM with LLMs and
then verifying the AWM based on agent experience not only increases sample
efficiency over contemporary methods by an order of magnitude but is also
robust to and corrects errors in the LLM, successfully blending noisy
internet-scale information from LLMs with knowledge grounded in environment
dynamics.
