Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Asynchronous Gradient Play in Zero-Sum Multi-agent Games
Authors: Ruicheng Ao, Shicong Cen, Yuejie Chi
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we verify our theoretical findings by investigating the performance of both single-timescale and two-timescale OMWU on randomly generated zero-sum entropy-regularized polymatrix games with n = 10, |Si| = 10, i V and τ = 0.1. For each (i, j) E, we set Aij = A ji with entries of Aij independently sampled from the uniform distribution over [ 1, 1]. All the results are averaged over five independent runs. In Fig. 1 (a), we compare the performance of single-timescale OMWU in both synchronous and asynchronous settings, with delay uniformly sampled from {0, 1, . . . , 10}. |
| Researcher Affiliation | Academia | Ruicheng Ao Peking University archer EMAIL Shicong Cen & Yuejie Chi Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Entropy-regularized OMWU, agent i |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the methodology or a link to a code repository. |
| Open Datasets | No | In this section, we verify our theoretical findings by investigating the performance of both single-timescale and two-timescale OMWU on randomly generated zero-sum entropy-regularized polymatrix games with n = 10, |Si| = 10, i V and τ = 0.1. For each (i, j) E, we set Aij = A ji with entries of Aij independently sampled from the uniform distribution over [ 1, 1]. |
| Dataset Splits | No | The paper describes using 'randomly generated' games for numerical experiments but does not specify any dataset splits (e.g., training, validation, test percentages or 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 specify any software components with version numbers used for its implementation or experiments. |
| Experiment Setup | Yes | In this section, we verify our theoretical findings by investigating the performance of both single-timescale and two-timescale OMWU on randomly generated zero-sum entropy-regularized polymatrix games with n = 10, |Si| = 10, i V and τ = 0.1. For each (i, j) E, we set Aij = A ji with entries of Aij independently sampled from the uniform distribution over [ 1, 1]. All the results are averaged over five independent runs. ... We adopt the optimal learning rate η from {0.1, 0.05, 0.02, 0.01, . . . } that yields the highest accuracy. |