AI RESEARCH
Imitation Learning for Combinatorial Optimisation under Uncertainty
arXiv CS.LG
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ArXi:2601.05383v4 Announce Type: replace Imitation learning (IL) provides a data-driven framework for approximating policies for large-scale combinatorial optimisation problems formulated as sequential decision problems (SDPs), where exact solution methods are computationally intractable. A central but underexplored aspect of IL in this context is the role of the \emph{expert} that generates