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We propose BOSS, an approach that automatically learns to solve newlong-horizon, complex, and meaningful tasks by growing a learned skill librarywith minimal supervision. Prior work in reinforcement learning require expertsupervision, in the form of demonstrations or rich reward functions, to learnlong-horizon tasks. Instead, our approach BOSS (BOotStrapping your own Skills)learns to accomplish new tasks by performing "skill bootstrapping," where anagent with a set of primitive skills interacts with the environment to practicenew skills without receiving reward feedback for tasks outside of the initialskill set. This bootstrapping phase is guided by large language models (LLMs)that inform the agent of meaningful skills to chain together. Through thisprocess, BOSS builds a wide range of complex and useful behaviors from a basicset of primitive skills. We demonstrate through experiments in realistichousehold environments that agents trained with our LLM-guided bootstrappingprocedure outperform those trained with naive bootstrapping as well as priorunsupervised skill acquisition methods on zero-shot execution of unseen,long-horizon tasks in new environments. Website at clvrai.com/boss.

Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance