AI RESEARCH
Representative Action Selection for Large Action Space Bandit Families
arXiv CS.LG
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ArXi:2505.18269v5 Announce Type: replace We study the problem of selecting a subset from a large action space shared by a family of bandits. In many natural situations, while the nominal set of actions is large, actions are highly correlated: many yield similar rewards across environments, making it wasteful to maintain the full set. Our aim is to understand whether it is possible -- and how -- to select a smaller set of representative actions that performs nearly as well as the full action space.