SlowGuess/NeurIPS_2023_OCR huggingface dataset:
In this paper, we study the problem of learning Nash equilibria in multi-agent decision-dependent games with access to the first-order oracle. We propose a parametric assumption to handle the distribution shift and develop a novel online algorithm 0PGD in both the linear and RKHS settings. We derive sufficient conditions to ensure the decision-dependent game is strongly monotone under the parametric assumption. We show that 0PGD converges to the Nash equilibrium at a rate of  $\mathcal{O}(t^{-1})$  in the linear setting and  $\mathcal{O}(t^{-\frac{\beta - \alpha}{\beta - \alpha + 2}})$  in the RKHS setting.
We would like to thank the area chair, and three anonymous reviewers for their very helpful comments, which help us substantially improve our paper. E. X. Fang is partially supported by NSF DMS-2230795 and DMS-2230797.
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