Potential-Aware Imperfect-Recall Abstraction with Earth Mover’s Distance in Imperfect-Information Games
Authors: Sam Ganzfried, Tuomas Sandholm
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on no-limit Texas Hold em show that our algorithm improves performance over the previously best approach. . . . In both experiments, our new approach led to a statistically significant improvement over the old approach. |
| Researcher Affiliation | Academia | Sam Ganzfried and Tuomas Sandholm Computer Science Department Carnegie Mellon University {sganzfri, sandholm}@cs.cmu.edu |
| Pseudocode | Yes | Our abstraction algorithm, depicted in Algorithm 1, works as follows. . . . Our heuristic is given in Algorithm 2. |
| Open Source Code | No | The paper does not include an unambiguous statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper evaluates an algorithm within the game of no-limit Texas Hold 'em and does not refer to a publicly available or open dataset with specific access information for training or evaluation. The 'data' mentioned for figures 1 and 2 is specific to those visualizations, not the core experimental setup. |
| Dataset Splits | No | The paper does not provide specific dataset split information (like percentages or sample counts for training, validation, and test sets) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper mentions 'parallelizing each step using 64 cores' but does not provide specific details such as CPU/GPU models, processor types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper mentions algorithms and external references (e.g., 'k-means++', 'counterfactual regret minimization', 'multi-dimensional EMD algorithm') but does not list specific software components with version numbers required for reproduction. |
| Experiment Setup | Yes | In both experiments, we used 169, 5000, 5000, and 5000 card buckets respectively in the four betting rounds for both the new algorithm and the prior algorithm. . . . For both flop abstraction algorithms, we conducted 25 restarts using the k-means++ initialization procedure (Arthur and Vassilvitskii 2007), and selected the run that produced the lowest within-cluster sum of squares. . . . In each of the two experiments, we ran 20,000 duplicate matches between our new agent and the respective benchmark agent. |