Roping in Uncertainty: Robustness and Regularization in Markov Games
Authors: Jeremy Mcmahan, Giovanni Artiglio, Qiaomin Xie
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study robust Markov games (RMG) with s-rectangular uncertainty. We show a general equivalence between computing a robust Nash equilibrium (RNE) of a s-rectangular RMG and computing a Nash equilibrium (NE) of an appropriately constructed regularized MG. The equivalence result yields a planning algorithm for solving s-rectangular RMGs, as well as provable robustness guarantees for policies computed using regularized methods. |
| Researcher Affiliation | Academia | 1University of Wisconsin-Madison, USA. Correspondence to: Jeremy Mc Mahan <jmcmahan@cs.wisc.edu>. |
| Pseudocode | No | The paper focuses on theoretical proofs and mathematical derivations, and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about making its source code publicly available or provide links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not report on empirical studies that would involve publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits or validation procedures as it does not conduct empirical experiments. |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software or libraries with version numbers used for implementation or experiments. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |