On Last-Iterate Convergence Beyond Zero-Sum Games
Authors: Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina, Tuomas Sandholm
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we numerically investigated the last-iterate convergence of (OMD) in two zero-sum extensive-form games (EFGs). ... The results are shown in Figure 1. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University 2University of California Irvine 3Strategy Robot, Inc. 4Optimized Markets, Inc. 5Strategic Machine, Inc. |
| Pseudocode | No | The paper describes algorithms using mathematical equations (e.g., OMD, MD, OGD update rules) but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | No | The paper does not provide a direct link to a code repository or an explicit statement about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We experimented on two standard poker benchmarks known as Kuhn poker (Kuhn, 1950) and Leduc poker (Southey et al., 2005). |
| Dataset Splits | No | The paper mentions running experiments on benchmark games like Kuhn poker and Leduc poker but does not specify explicit training/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') required to replicate the experiments. |
| Experiment Setup | Yes | We ran (OMD) with Euclidean regularization and three different values of η for 10000 iterations. |