Last-iterate Convergence in Extensive-Form Games
Authors: Chung-Wei Lee, Christian Kroer, Haipeng Luo
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide experiments to further support our theoretical results. |
| Researcher Affiliation | Academia | Chung-Wei Lee University of Southern California leechung@usc.edu Christian Kroer Columbia University christian.kroer@columbia.edu Haipeng Luo University of Southern California haipengl@usc.edu |
| Pseudocode | No | The paper describes algorithms using mathematical equations and prose but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | In this section, we experimentally evaluate the algorithms on three standard EFG benchmarks: Kuhn poker [Kuhn, 1950], Pursuit-evasion [Kroer et al., 2018], and Leduc poker [Southey et al., 2005]. |
| Dataset Splits | No | The paper describes the game environments used for experiments but does not provide specific training/validation/test dataset splits or methodologies, as these are game simulations rather than typical static datasets with pre-defined splits. |
| Hardware Specification | No | The paper states: 'All the experiments are run on CPU in a personal computer' but does not provide specific details on the CPU model, memory, or other hardware components. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For each optimistic algorithm, we fine-tune step size η to get better convergence results and show its value in the legends. |