Learning in Congestion Games with Bandit Feedback

In this paper, we investigate Nash-regret minimization in congestion games, a class of games with benign theoretical structure and broad real-world applications. We first propose a centralized algorithm based on the optimism in the face of uncertainty principle for congestion games with (semi-)bandi...

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Hauptverfasser:	Cui, Qiwen, Xiong, Zhihan, Fazel, Maryam, Du, Simon S
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computer Science and Game Theory Computer Science - Learning Computer Science - Multiagent Systems Statistics - Machine Learning
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