A contextual optimistic multiplicative weights algorithm (POMWU) achieves static-game regret, equilibrium convergence, and social welfare guarantees in time-varying games when players can predict the changing state of nature with bounded error.
Uncoupled learning dynamics with o (log t) swap regret in multiplayer games
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Prediction-Aware Learning in Multi-Agent Systems
A contextual optimistic multiplicative weights algorithm (POMWU) achieves static-game regret, equilibrium convergence, and social welfare guarantees in time-varying games when players can predict the changing state of nature with bounded error.