An ε-agnostic algorithm for fixed-budget ε-good max-min action identification in depth-2 trees achieves misidentification probability decaying as exp(-~Θ(T/H₂(ε))).
either all ε/2-good subtrees are nonempty, or all ε-bad subtrees are empty
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$\varepsilon$-Good Action Identification in Fixed-Budget Monte Carlo Tree Search
An ε-agnostic algorithm for fixed-budget ε-good max-min action identification in depth-2 trees achieves misidentification probability decaying as exp(-~Θ(T/H₂(ε))).