On Tractable $\Phi$-Equilibria in Non-Concave Games
Authors: Yang Cai, Constantinos Daskalakis, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we initiate the study of tractable Φ-equilibria in nonconcave games and examine several natural families of strategy modifications. We show that when Φ is finite, there exists an efficient uncoupled learning algorithm that approximates the corresponding Φ-equilibria. Additionally, we explore cases where Φ is infinite but consists of local modifications, showing that Online Gradient Descent can efficiently approximate Φ-equilibria in non-trivial regimes. |
| Researcher Affiliation | Academia | Yang Cai Yale University yang.cai@yale.edu Constantinos Daskalakis MIT CSAIL costis@csail.mit.edu Haipeng Luo University of Southern California haipengl@usc.edu Chen-Yu Wei University of Virginia chenyu.wei@virginia.edu Weiqiang Zheng Yale University weiqiang.zheng@yale.edu |
| Pseudocode | Yes | Algorithm 1: Φ-regret minimization for non-concave reward via sampling; Algorithm 2: SAMPLESTRATEGY; Algorithm 3: Conv(Φ)-regret minimization for Lipschitz smooth non-concave rewards |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | This paper does not contain experimental results and therefore does not use a dataset. |
| Dataset Splits | No | This paper does not contain experimental results and therefore does not define dataset splits. |
| Hardware Specification | No | This paper does not contain experimental results and therefore does not provide hardware specifications. |
| Software Dependencies | No | This paper does not contain experimental results and therefore does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This paper does not contain experimental results and therefore does not provide details on experimental setup or hyperparameters. |