Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms
Authors: Xuchuang Wang, Hong Xie, John C. S. Lui
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate our algorithm s performance and also its gain in 5G & 4G base station selection. We conduct simulations to validate the performance of Orch Explore in Algorithm 1 and compare it to other algorithms adapted from MAB. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, The Chinese University of Hong Kong 2College of Computer Science, Chongqing University, China. |
| Pseudocode | Yes | Algorithm 1 Orchestrative Exploration Orch Explore. Algorithm 2 Multiple-play successive elimination with shareable arms (MP-SE-SA). Algorithm 3 Procedures of MP-SE-SA. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper uses a custom-defined set of parameters for its simulations (Section 8) and parameters derived from 'Narayanan et al. (2020)' for a 'real-world' application (Appendix I.1), but it does not provide access to a publicly available dataset in the conventional sense (e.g., a downloadable data file). |
| Dataset Splits | No | The paper conducts simulations based on defined parameters rather than using a dataset with explicit training, validation, and test splits. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models) used for running its simulations or experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, specific libraries or solvers). |
| Experiment Setup | Yes | We set δ = 2/T as default. In simulation (Section 8 and Appendix I), we set both equal to 1 as default. |