Action subsampling supports policy compression in large action spaces.

ABSTRACT: Real-world decision-making often involves navigating large action spaces with state-dependent action values, taxing the limited cognitive resources at our disposal. While previous studies have explored cognitive constraints on generating action consideration sets or refining state-action mappings (policy complexity), their interplay remains underexplored. In this work, we present a resource-rational framework for policy compression that integrates both constraints, offering a unified perspective on decision-making under cognitive limitations. Through simulations, we characterize the suboptimality arising from reduced action consideration sets and reveal the complex interaction between policy complexity and action consideration set size in mitigating this suboptimality. We then use such nor

SUBMITTER: Liu S 

PROVIDER: S-EPMC12422588 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature