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..
Regret Minimization and Convergence to Equilibria in General-sum Markov Games
Authors: Liad Erez, Tal Lancewicki, Uri Sherman, Tomer Koren, Yishay Mansour
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we present the first (to our knowledge) algorithm for learning in general-sum Markov games that provides sublinear regret guarantees when executed by all agents. The bounds we obtain are for swap regret, and thus, along the way, imply convergence to a correlated equilibrium. Our algorithm is decentralized, computationally efficient, and does not require any communication between agents. |
| Researcher Affiliation | Collaboration | 1Blavatnik School of Computer Science, Tel Aviv University, Israel 2Google Research, Tel Aviv. |
| Pseudocode | Yes | Algorithm 1 Policy Optimization by Swap Regret Minimization |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available, in supplementary materials, or in a repository. |
| Open Datasets | No | The paper is theoretical and does not describe or use any specific dataset for training. Therefore, it does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets. As such, it does not provide specific dataset split information for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used to run experiments or computations. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers required to replicate experiments or computations. |
| Experiment Setup | No | The paper is theoretical and describes algorithm parameters for its analytical bounds (e.g., 'parameter 𝛾> 0, learning rate 𝜂> 0, regularizer 𝑅( )' and specific choices like '𝜂= 1 96𝐻2𝑚𝑆𝐴' for theorems), but it does not provide an experimental setup with hyperparameters or system-level training settings for actual experiments. |