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..
Smooth Fictitious Play in Stochastic Games with Perturbed Payoffs and Unknown Transitions
Authors: Lucas Baudin, Rida Laraki
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove the convergence of our family of procedures to stationary regularized Nash equilibria in zero-sum and identical-interest discounted stochastic games. The proof uses the continuous smooth best-response dynamics counterparts, and stochastic approximation methods. |
| Researcher Affiliation | Academia | Lucas Baudin Université Paris-Dauphine PSL EMAIL Rida Laraki CNRS, Université Paris-Dauphine PSL University of Liverpool |
| Pseudocode | No | The paper describes mathematical systems and update rules (e.g., equations for ui and xs) but does not present them in a formal pseudocode block or algorithm format. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide links to a code repository. The checklist indicates 'N/A' for questions regarding code availability. |
| Open Datasets | No | The paper primarily presents theoretical results and proofs. While Appendix C mentions an example with empirical results, the paper does not describe the use of any specific public dataset, nor does it provide any links or formal citations to a dataset for training. |
| Dataset Splits | No | The paper focuses on theoretical convergence proofs and does not describe any training, validation, or test dataset splits. The checklist explicitly states 'N/A' for training details including data splits. |
| Hardware Specification | No | The paper is theoretical and does not provide any specific hardware specifications (e.g., GPU/CPU models, memory) used for running experiments. The checklist states 'N/A' for details on compute resources. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers that would be required to replicate experiments. The checklist states 'N/A' for training details. |
| Experiment Setup | No | The paper presents theoretical models and proofs rather than an empirical experimental setup. It does not provide specific hyperparameter values, training configurations, or system-level settings for experiments. The checklist states 'N/A' for training details. |