Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Regret Minimization in Behaviorally-Constrained Zero-Sum Games
Authors: Gabriele Farina, Christian Kroer, Tuomas Sandholm
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,786 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,114 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments to investigate the practical performance of our perturbed-regret-minimization approach when used to instantiate the CFR and CFR+ algorithms for computing approximate EFPE in EFGs. We compare these algorithms to state-of-the-art Nash-equilibrium-finding algorithms... |
| Researcher Affiliation | Academia | Carnegie Mellon University, Pittsburgh PA 15213 USA. Correspondence to: Gabriele Farina <EMAIL>, Christian Kroer <EMAIL>, Tuomas Sandholm <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 RM+ algorithm for generalized normal-form games played over finitely-generated convex polytopes. and Algorithm 2 Regret minimization algorithm for perturbed extensive-form games. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the methodology described in this paper is publicly available. |
| Open Datasets | Yes | We conducted the experiments on Leduc hold em poker (Southey et al., 2005), a widely-used benchmark in the imperfect-information game-solving community. |
| Dataset Splits | No | The paper describes the game setup and perturbations (e.g., 'k={3,5}' and 'uniform perturbations p(I, a) = ξ for all information sets I and actions a A(I), for ξ {0.1, 0.05, 0.01, 0.005, 0.001}'), but does not provide specific training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments were mentioned. |
| Software Dependencies | No | The paper refers to various algorithms and techniques, but does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their versions) required for replication. |
| Experiment Setup | Yes | We test our approach on games subject to different uniform perturbations p(I, a) = ξ for all information sets I and actions a A(I), for ξ {0.1, 0.05, 0.01, 0.005, 0.001}. |