Asynchronous Gradient Play in Zero-Sum Multi-agent Games
Authors: Ruicheng Ao, Shicong Cen, Yuejie Chi
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 arc@pku.edu.cn Shicong Cen & Yuejie Chi Carnegie Mellon University {shicongc,yuejiec}@andrew.cmu.edu |
| 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. |