- Conference Paper
We formulate the novel class of contextual games, a type of repeated games driven by contextual information at each round. By means of kernel-based regularity assumptions, we model the correlation between different contexts and game outcomes and propose a novel online (meta) algorithm that exploits such correlations to minimize the contextual regret of individual players. We define game-theoretic notions of contextual Coarse Correlated Equilibria (c-CCE) and optimal contextual welfare for this new class of games and show that c-CCEs and optimal 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 to baselines that do not exploit the available contextual information or the correlations present in the game. Show more
Book titleAdvances in Neural Information Processing Systems 33
Pages / Article No.
Organisational unit03908 - Krause, Andreas / Krause, Andreas
09578 - Kamgarpour, Maryam / Kamgarpour, Maryam
815943 - Reliable Data-Driven Decision Making in Cyber-Physical Systems (EC)
19-2 FEL-47 - Robust Sample-Efficient Learning when Data ist Costly (ETHZ)
NotesDue to the Coronavirus (COVID-19) the conference was conducted virtually.
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