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We formulate the novel class of contextual games, a type of repeated gamesdriven by contextual information at each round. By means of kernel-basedregularity assumptions, we model the correlation between different contexts andgame outcomes and propose a novel online (meta) algorithm that exploits suchcorrelations to minimize the contextual regret of individual players. We definegame-theoretic notions of contextual Coarse Correlated Equilibria (c-CCE) andoptimal contextual welfare for this new class of games and show that c-CCEs andoptimal welfare can be approached whenever players' contextual regrets vanish.Finally, we empirically validate our results in a traffic routing experiment,where our algorithm leads to better performance and higher welfare compared tobaselines that do not exploit the available contextual information or thecorrelations present in the game.

Contextual Games: Multi-Agent Learning with Side Information