Refining Subgames in Large Imperfect Information Games
Authors: Matej Moravcik, Martin Schmid, Karel Ha, Milan Hladik, Stephen Gaukrodger
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our technique using one of the top participants of the AAAI-14 Computer Poker Competition, the leading playground for agents in imperfect information settings. For the first time, we evaluate these techniques on the safe-refinement task as part of a large-scale game by using one of the top participating agents in AAAI-14 Computer Poker Competition as the baseline strategy to be refined in subgames. |
| Researcher Affiliation | Collaboration | Matej Moravcik, Martin Schmid, Karel Ha, Milan Hladik Charles University In Prague {moravcim, schmidm, karelha, hladik} @kam.mff.cuni.cz, Stephen J. Gaukrodger Koypetition stephen@koypetition.com |
| Pseudocode | No | The paper describes steps for its gadget construction but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper mentions using a participant of the 'AAAI-14 Computer Poker Competition' and 'heads-up no-limit Texas Hold em Total Bankroll' as the game environment, but it does not specify a publicly available dataset with concrete access information (link, DOI, formal citation) in the traditional sense of a machine learning dataset. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits (percentages, sample counts, or explicit references to predefined splits). |
| Hardware Specification | No | The paper mentions 'Computational resources were provided by the Meta Centrum under the program LM2010005 and the CERIT-SC under the program Centre CERIT Scientific Cloud, part of the Operational Program Research and Development for Innovations, Reg. no. CZ.1.05/3.2.00/08.0144.' This indicates the use of computational resources but does not provide specific hardware details like CPU or GPU models. |
| Software Dependencies | No | The paper mentions 'CFR+' and 'domain-specific speedup tricks' but does not provide specific software names with version numbers for reproducibility (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We ran 10, 000 iterations of the CFR+ algorithm in the corresponding gadget games. Exponential weighting is used to update the average strategies (Tammelin et al. 2015). Each technique was used to refine around 2, 000 subgames. |