Optimal Arms Identification with Knapsacks
Authors: Shaoang Li, Lan Zhang, Yingqi Yu, Xiangyang Li
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Numerical Evaluations We consider a specific instance in which there are four arms (m = 4), three types of resources (d = 3), the expected per-round resource constraint of 0 < b 1, and a parameter 0 < ϵ b. The unknown reward vector is r = (0.5, 0.5 ϵ, 0.5, 0.5), and the unknown expected resource consumption is represented by the matrix: Figure 1. The results obtained in different environments. ... The results (accuracy) obtained in different environments are summarized in Figure 1. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China, Hefei, China. |
| Pseudocode | Yes | Algorithm 1 Base OAK Algorithm (BASEOAK) ... Algorithm 2 Full OAK Algorithm (FULLOAK) ... Algorithm 3 BASEOAK |
| Open Source Code | Yes | The code for algorithms could be available at https://github.com/Shaoang Li/ OAK-problem.git. |
| Open Datasets | No | The paper describes a simulated environment/instance rather than using a named, publicly available dataset with concrete access information. 'We consider a specific instance in which there are four arms (m = 4), three types of resources (d = 3), the expected per-round resource constraint of 0 < b 1, and a parameter 0 < ϵ b.' |
| Dataset Splits | No | The paper describes a simulation setup and varying parameters (T, b, ϵ) but does not mention specific training, validation, or test dataset splits or cross-validation for a defined dataset. |
| Hardware Specification | No | The paper describes the setup for numerical evaluations but does not mention any specific hardware components (e.g., CPU, GPU models, memory, or cloud instance types) used for running the simulations. |
| Software Dependencies | No | The paper provides a link to GitHub, implying code for the algorithms. However, it does not explicitly list any specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We consider a specific instance in which there are four arms (m = 4), three types of resources (d = 3), the expected per-round resource constraint of 0 < b 1, and a parameter 0 < ϵ b. The unknown reward vector is r = (0.5, 0.5 ϵ, 0.5, 0.5), and the unknown expected resource consumption is represented by the matrix: ... We begin by considering the case where the knapsack b = 0.2 and the gap ϵ = 0.01. ... we vary the value of ϵ while keeping b = 0.2 and T = 2 * 10^4 fixed... We conduct experiments with ϵ = 0.01 and T = 2 * 10^4 fixed... |