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..
Convex-Concave Zero-Sum Markov Stackelberg Games
Authors: Denizalp Goktas, Arjun Prakash, Amy Greenwald
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also prove that reach-avoid problems are naturally modeled as convex-concave zero-sum Markov Stackelberg games, and show experimentally that Stackelberg equilibrium policies are more effective than their Nash counterparts in these problems.1 |
| Researcher Affiliation | Academia | Denizalp Goktas Brown University, Computer Science EMAIL Arjun Prakash Brown University, Computer Science EMAIL Amy Greenwald Brown University, Computer Science EMAIL |
| Pseudocode | Yes | Algorithm 1 Saddle-Point-Oracle SGD/Nested SGDA |
| Open Source Code | Yes | Our code is found at: https://github.com/arjun-prakash/stackelberg-reach-avoid. |
| Open Datasets | No | The paper describes the setup for a reach-avoid game with specific parameters but does not refer to a publicly available dataset by name or provide access information (link, DOI, or specific citation). |
| Dataset Splits | No | The paper describes running '100 games' for evaluation but does not specify dataset splits (e.g., percentages or counts for training, validation, or testing). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with versions). |
| Experiment Setup | Yes | Our experiments were run on a 7x7 square grid, with the target set T a closed ball of radius 1 centered along the lower edge, and the avoid set V a closed ball of radius 0.3 around the antagonist. We set the bonus (resp. penalty) for reaching the target (resp. avoid set) β = 200, ! = 30 , and = 0.25. |