Reinforcement Learning and Regret Bounds for Admission Control
Authors: Lucas Weber, Ana Busic, Jiamin Zhu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations are conducted in section 7 |
| Researcher Affiliation | Collaboration | 1Inria and DI ENS, Ecole Normale Sup erieure, PSL University, Paris, France 2IFP Energies nouvelles, 1 et 4 avenue de Bois-Pr eau, 92852 Rueil-Malmaison, France. |
| Pseudocode | Yes | Algorithm 1 PI for Admission Control; Algorithm 2 VI for Admission Control; Algorithm 3 UCRL-AC |
| Open Source Code | Yes | Our code is available at: https://github.com/luweber21/ucrl-ac. |
| Open Datasets | No | The paper describes a simulated queuing system (M/M/c/S queue) and its parameters rather than using a conventional publicly available dataset. |
| Dataset Splits | No | The paper simulates a queuing system and does not specify train/validation/test splits as it does not rely on a conventional dataset. |
| Hardware Specification | Yes | The experiments were run on a Mac Book Pro 2021 equipped with an Apple M1 Pro processor and 16 GB RAM. |
| Software Dependencies | No | The paper does not specify version numbers for key software components or libraries used in the experiments. |
| Experiment Setup | Yes | We consider 5 servers, 2 job classes with immediate rewards R1 = 20 and R2 = 10 and arrival rates λ1 = 1 and λ2 = 1 respectively, and holding cost C(t) = 0.1t for both classes. For UCRL-AC, we used Λmin = 1 and Λmax = 4. |