Regret Minimization in Behaviorally-Constrained Zero-Sum Games
Authors: Gabriele Farina, Christian Kroer, Tuomas Sandholm
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 <gfarina@cs.cmu.edu>, Christian Kroer <ckroer@cs.cmu.edu>, Tuomas Sandholm <sandholm@cs.cmu.edu>. |
| 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}. |