Budget-Constrained Multi-Armed Bandits With Multiple Plays
Authors: Datong Zhou, Claire Tomlin
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Firstly, we analyze this setting for the stochastic case... We derive an Upper Confidence Bound (UCB) algorithm which achieves O(NK4 log B) regret. Secondly, for the adversarial case... we derive an upper bound on the regret of order O(NB log(N/K)) utilizing an extension of the well-known Exp3 algorithm. |
| Researcher Affiliation | Academia | Datong P. Zhou,1 Claire J. Tomlin2 Dept. of Mechanical Engineering, 2Dept. of Electrical Engineering and Computer Sciences University of California, Berkeley, CA 94720 {datong.zhou, tomlin}@berkeley.edu |
| Pseudocode | Yes | Algorithm 1 UCB-MB for Stochastic MAB |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information (e.g., percentages, counts) for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameter values or training configurations. |