  Reinforcement learning algorithms typically struggle in the absence of a
dense, well-shaped reward function. Intrinsically motivated exploration methods
address this limitation by rewarding agents for visiting novel states or
transitions, but these methods offer limited benefits in large environments
where most discovered novelty is irrelevant for downstream tasks. We describe a
method that uses background knowledge from text corpora to shape exploration.
This method, called ELLM (Exploring with LLMs) rewards an agent for achieving
goals suggested by a language model prompted with a description of the agent's
current state. By leveraging large-scale language model pretraining, ELLM
guides agents toward human-meaningful and plausibly useful behaviors without
requiring a human in the loop. We evaluate ELLM in the Crafter game environment
and the Housekeep robotic simulator, showing that ELLM-trained agents have
better coverage of common-sense behaviors during pretraining and usually match
or improve performance on a range of downstream tasks. Code available at
https://github.com/yuqingd/ellm.
