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.
Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory Meets Game Theory
Authors: Stefanos Leonardos, Georgios Piliouras11263-11271
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,698 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,822 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 | The above ο¬ndings are visualized in systematic experiments in both low and large dimensional games along two representative exploration-exploitation policies, explore-thenexploit and cyclical learning rates (Experiments Section). |
| Researcher Affiliation | Academia | Stefanos Leonardos, Georgios Piliouras Singapore University of Technology and Design, 8 Somapah Road, 487372 Singapore, {stefanos leonardos ; georgios}@sutd.edu.sg |
| Pseudocode | No | The paper provides mathematical equations and derivations, but no structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | No explicit statement or link regarding the availability of open-source code for the described methodology was found. |
| Open Datasets | No | The experiments are conducted on coordination games and randomly generated potential games, but no specific publicly available or open dataset with access information (link, DOI, or formal citation) is provided. |
| Dataset Splits | No | The paper describes experimental scenarios but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., programming languages, libraries, solvers) used for implementation. |
| Experiment Setup | No | The paper describes the general policies (ETE, CLR-1) and their behavior, but lacks specific numerical hyperparameters, training configurations, or system-level settings for the experiments. |