Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory Meets Game Theory
Authors: Stefanos Leonardos, Georgios Piliouras11263-11271
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The above findings 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. |