Going Beyond State-Reaching: Learning Abstractions for Intrinsically Motivated Option Discovery

Abstract:Temporal abstraction via options can improve exploration in large environments. However, existing option discovery algorithms find subgoals that target all aspects of the state simultaneously. This state-reaching approach produces options that only apply in narrow regions of the state-space, eventually causing an explosion in the number of options that overwhelms the agent, and impedes progress on its primary task of reward maximization. We introduce an algorithm that instead identifies a small, relevant subset of features for each subgoal, yielding options that generalize broadly and accelerate exploration. Our approach learns abstract, transferrable options and achieves rapid exploration in three sparse-reward, image-based domains, including the Atari game MontezumasRevenge.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.36473 [cs.AI]

(or arXiv:2609.36473v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.36473 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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