Smoothing Method for Approximate Extensive-Form Perfect Equilibrium
Authors: Christian Kroer, Gabriele Farina, Tuomas Sandholm
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our smoothing approach leads to solutions with dramatically stronger strategies at information sets that are reached with low probability in approximate Nash equilibria, while retaining the overall convergence rate associated with fast algorithms for Nash equilibrium. |
| Researcher Affiliation | Academia | Christian Kroer and Gabriele Farina and Tuomas Sandholm Computer Science Department Carnegie Mellon University {ckroer,gfarina,sandholm}@cs.cmu.edu |
| Pseudocode | Yes | ALGORITHM 1: EGT |
| Open Source Code | No | No explicit statement about making the source code for their methodology publicly available was found. |
| 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, except we tested on a larger variant of the game in order to better test scalability. |
| Dataset Splits | No | The paper mentions "We tune an overall weight on each DGF by choosing the weight that performs best with EGT and ξ = 0 among 1, 0.1, 0.05, 0.01, 0.005 on the first 20 iterations," but does not specify explicit train/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments are provided. |
| Software Dependencies | No | The paper mentions algorithms like CFR and EGT but does not provide specific software dependencies (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We tune an overall weight on each DGF by choosing the weight that performs best with EGT and ξ = 0 among 1, 0.1, 0.05, 0.01, 0.005 on the first 20 iterations. We test our approach on ξ-perturbed polytopes of the strategy spaces for ξ {0.1, 0.05, 0.01, 0.005, 0.001}. |