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..
Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning
Authors: Noam Brown, Tuomas Sandholm
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that BRP results in a factor of 7 reduction in space, and the reduction factor increases with game size. |
| Researcher Affiliation | Academia | 1Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA. Correspondence to: Noam Brown <EMAIL>, Tuomas Sandholm <EMAIL>. |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly provided in the paper. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The experiments are conducted on Leduc Hold em (Southey et al., 2005) and Leduc-5 (Brown & Sandholm, 2015a). |
| Dataset Splits | No | The paper evaluates performance within game environments (Leduc Hold'em, Leduc-5) and discusses convergence over iterations, but does not define traditional train/validation/test dataset splits as it's not a typical supervised learning setup with fixed datasets. |
| Hardware Specification | No | The paper uses 'Nodes touched' as a hardware and implementation-independent proxy for time and does not provide specific details on the hardware used for experiments. |
| Software Dependencies | No | The paper discusses various algorithms (CFR, CFR+, RM, Fictitious Play) but does not specify any software dependencies or library version numbers used for implementation. |
| Experiment Setup | Yes | Figure 1 and Figure 2 show the reduction in space needed to store the average strategy and regrets for BRP for various values of the constant threshold C, where an action s probability is set to zero if it is reached with probability less than C T in the average strategy, as we explained in Section 3.1. In both games, a threshold between 0.01 and 0.1 performed well in both space and number of iterations, with the lower thresholds converging somewhat faster and the higher thresholds reducing space faster. |