Towards General Function Approximation in Zero-Sum Markov Games
Authors: Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper considers two-player zero-sum finite-horizon Markov games with simultaneous moves. The study focuses on the challenging settings where the value function or the model is parameterized by general function classes. Provably efficient algorithms for both decoupled and coordinated settings are developed. |
| Researcher Affiliation | Academia | Baihe Huang School of Mathematical Sciences Peking University baihehuang@pku.edu.cn Jason D. Lee Department of Electrical and Computer Engineering Princeton University Jasondl@princeton.edu Zhaoran Wang Departments of Industrial Engineering & Management Sciences Northwestern University zhaoranwang@gmail.com Zhuoran Yang Department of Statistics and Data Science Yale University zhuoran.yang@yale.edu |
| Pseudocode | Yes | Algorithm 1 Optimistic Nash Elimination for Markov Games (ONEMG) |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on algorithms and sample complexity bounds, rather than reporting experiments on specific publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for experimental reproduction. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe a concrete experimental setup with hyperparameters or system-level training settings. |