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Learning in Congestion Games with Bandit Feedback. (arXiv:2206.01880v2 [cs.GT] UPDATED)
Oct. 17, 2022, 1:14 a.m. | Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S. Du
stat.ML updates on arXiv.org arxiv.org
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-)bandit
feedback, and obtain finite-sample guarantees. Then we propose a decentralized
algorithm via a novel combination of the Frank-Wolfe method and G-optimal
design. By exploiting the structure of the congestion game, we show the sample
complexity of both …
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