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..
Beyond Strict Competition: Approximate Convergence of Multi-agent Q-Learning Dynamics
Authors: Aamal Hussain, Francesco Belardinelli, Georgios Piliouras
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As our experiments show, these guarantees are independent of whether the dynamics ultimately reach an equilibrium, or remain non-convergent. Also, section 4 is titled "Experiments on Near NZSG". |
| Researcher Affiliation | Academia | Aamal Hussain1 , Francesco Belardinelli1 , Georgios Piliouras2 1Imperial College London 2Singapore University of Technology and Design EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability or links to code repositories. |
| Open Datasets | No | The paper states that they "generate a two-action, zero-sum network game" and "perturb the payoff matrices to generate five near zero-sum games." This indicates they generated their own data rather than using a publicly available dataset. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. They generate game instances and observe dynamics, but do not mention specific percentages or sample counts for data partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In all cases, T = 0.75. Then, we perturb the payoff matrices to generate five near zero-sum games. When examining the effect of noise, we take the same network game setup and periodically (every 50 iterations) add noise to the payoff matrices. Figure 4: after 1 x 10^6 iterations. |