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..
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
Authors: Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Vaggos Chatziafratis, Stelios Andrew Stavroulakis
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is the first algorithm for computing stationary ϵ-approximate Nash equilibria in adversarial team Markov games with computational complexity that is polynomial in all the natural parameters of the game, as well as 1/ϵ. The proposed algorithm is based on performing independent policy gradient steps for each player in the team, in tandem with best responses from the side of the adversary; in turn, the policy for the adversary is then obtained by solving a carefully constructed linear program. Our analysis leverages non-standard techniques to establish the KKT optimality conditions for a nonlinear program with nonconvex constraints, thereby leading to a natural interpretation of the induced Lagrange multipliers. |
| Researcher Affiliation | Academia | Fivos Kalogiannis UC Irvine Ioannis Anagnostides Carnegie Mellon University Ioannis Panageas UC Irvine Emmanouil V. Vlatakis-Gkaragkounis Columbia University Vaggos Chatziafratis UC Santa Cruz Stelios Stavroulakis UC Irvine |
| Pseudocode | Yes | Algorithm 1 Independent Policy Gradient Max (IPGMAX) |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available or providing a link to a code repository. |
| Open Datasets | No | The paper describes a theoretical framework and algorithm and does not include empirical experiments or discuss datasets. |
| Dataset Splits | No | The paper focuses on theoretical contributions and does not describe experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific details such as hyperparameters or training configurations. |