Author

Anita de Mello Koch

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#artificial intelligence Preprint Sep 2026

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

This work introduces an algorithm that identifies a small, relevant subset of features for each subgoal, yielding options that generalize broadly and accelerate exploration in three sparse-reward, image-based domains, including the Atari game MontezumasRevenge.

Akhil Bagaria, Anita de Mello Koch, G. Konidaris · 1 citation

Efficient Search by Tentatively Pruning Objects from Planning Tasks

This work introduces tentative object pruning, an approach that leverages this property by constructing multiple simplified tasks with reduced object sets and searching them in parallel until a valid, satisficing solution is found.

Anita de Mello Koch, Naman Shah, Cameron S. Allen et al. · 0 citations