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.
Generalized Rapid Action Value Estimation
Authors: Tristan Cazenave
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 17,027 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,692 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 test the resulting algorithm named GRAVE for Atarigo, Knighthrough, Domineering and Go. |
| Researcher Affiliation | Academia | Tristan Cazenave LAMSADE Universite Paris-Dauphine Paris, France EMAIL |
| Pseudocode | Yes | Algorithm 1 The GRAVE algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper discusses using various games (Atarigo, Knightthrough, Domineering, Go) as environments for experimentation, but does not provide specific access information (links, DOIs, citations to predefined splits) for publicly available datasets in the typical machine learning sense. |
| Dataset Splits | No | The paper mentions tuning parameters for RAVE and GRAVE, which implies a form of validation, but it does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning for training/validation/test sets. |
| Hardware Specification | No | The paper states: 'This work was granted access to the HPC resources of Meso PSL...', but it does not provide specific details such as exact GPU/CPU models, processor types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | the RAVE bias is ο¬rst tuned playing different RAVE bias against UCT with a 0.4 exploration parameter (the exploration parameter used for GGP), then the GRAVE bias as well as the ref constant are tuned against the tuned RAVE. ... We test as bias all the powers of 10 between 10 1 and 10 15. Additionally the ref constants tested for GRAVE are 25, 50, 100, 200 and 400. ... The algorithms are tested for 1,000 and 10,000 playouts. |