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About this paper
Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus
POMWU lets agents forecast hidden contexts before choosing strategies, then update using the actual context revealed after play. Under finite-context and bounded-cost assumptions, its regret depends on context-prediction mistakes; with realizable finite-Littlestone-dimension predictors, it recovers the static-game regret rate. In a Sioux Falls simulation with five contexts, it qualitatively outperforms OMWU, but this is not real-world evidence.
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