Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem
Authors: Fivos Kalogiannis, Jingming Yan, Ioannis Panageas
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Question: Does the paper conduct EMPIRICAL STUDIES WITH DATA ANALYSIS (experiments, dataset evaluation, performance metrics, or hypothesis validation) rather than purely theoretical work? Answer: [No] Justification: The paper is theoretical in nature and does not include experiments. |
| Researcher Affiliation | Academia | Fivos Kalogiannis University of California, Irvine Archimedes/Athena RC, Greece Jingming Yan University of California, Irvine Ioannis Panageas University of California, Irvine Archimedes/Athena RC, Greece |
| Pseudocode | Yes | Algorithm 1 Independent Stochastic Policy-Nested-Gradient (ISPNG) Algorithm 2 Visitation-Regularized Policy Gradient Algorithm (VIS-REG-PG) |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments, so it does not discuss training datasets or their public availability. |
| Dataset Splits | No | The paper does not include experiments, so it does not discuss training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not include experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper does not include experiments, so no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper does not include experiments, so it does not detail specific experimental setup, hyperparameters, or training configurations. |