Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposability
Authors: Revan MacQueen, James Wright
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 31,382 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,585 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our findings through experiments on Leduc poker. |
| Researcher Affiliation | Academia | Revan Mac Queen Department of Computing Science University of Alberta / Amii; James R. Wright Department of Computing Science University of Alberta / Amii |
| Pseudocode | Yes | Algorithm 1 SGDecompose; Algorithm 2 Compute γ; Algorithm 3 SGDecompose with behavior strategies; Algorithm 4 get BRs |
| Open Source Code | Yes | The codebase for our experiments is available at https://github.com/Revan Mac Queen/ Self-Play-Polymatrix. |
| Open Datasets | Yes | Leduc poker was originally developed for two players but was extended to a 3-player variant by Abou Risk & Szafron (2010); we use the 3-player variant here. |
| Dataset Splits | No | The paper does not specify explicit train/validation/test dataset splits in the context of supervised learning, as it focuses on learning through self-play in games. |
| Hardware Specification | No | The paper mentions 'Computation for this work was provided by the Digital Research Alliance of Canada' but does not specify exact hardware details such as GPU/CPU models or memory amounts. |
| Software Dependencies | No | The paper mentions software components like 'CFR+' and 'Open Spiel implementation' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | We used the same parameters for each run of SGDecompose: λ = 0.5, B = 30, T = 200. We used a learning rate schedule where the learning rate η begins at 2 −6, then halves every 5 epochs until reaching 2 −17 to encourage convergence. We randomly initialize CFR+ with regrets between 0 and 0.001 chosen uniformly at random, which are the default values in Open Spiel. |