Piecewise-Stationary Bandits with Knapsacks
Authors: Xilin Zhang, Wang Chi Cheung
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run numerical experiments on a single-resource problem where L 2, T 20000 (each stationary piece has 10000 rounds), K t1, 2u, B 9360 and we set α e for our algorithms. The rewards and resource consumption in all rounds are uniformly distributed within a r 0.2, 0.2s range from their mean values. We compare the performance of IRES-CM with Immorlica et al. (2019) s algorithm and Zhou et al. (2008) s algorithm. Recall that Immorlica et al. (2019) focus on an adversarial Bwk problem and achieves a CR w.r.t. a static benchmark. Zhou et al. (2008) study a full-feedback adversarial setting and achieves a CR w.r.t. a single best arm benchmark. In Figure 1, each curve represents the average cumulative reward over 10 simulations, and the shaded area around each curve marks the variance over the simulations. |
| Researcher Affiliation | Academia | Xilin Zhang Department of ISEM National University of Singapore Singapore, 117578 zhangxilin@u.nus.edu Cheung Wang Chi Department of ISEM National University of Singapore Singapore, 117578 isecwc@nus.edu.sg |
| Pseudocode | Yes | Algorithm 1 Inventory REServing with deterministic input (IRES) ... Algorithm 2 Inventory REServing with Change Monitoring (IRES-CM) |
| Open Source Code | No | Our data are numerically generated and codes can be provided. |
| Open Datasets | No | The paper generates its own data for numerical experiments: 'The rewards and resource consumption in all rounds are uniformly distributed within a r 0.2, 0.2s range from their mean values.' |
| Dataset Splits | No | The paper describes simulation parameters and runs, but does not specify a train/validation/test dataset split as it uses generated data in an online learning setting. |
| Hardware Specification | Yes | Finally, our experiments are run on a Surface Pro 7 with an i5-1035G4 processor. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We run numerical experiments on a single-resource problem where L 2, T 20000 (each stationary piece has 10000 rounds), K t1, 2u, B 9360 and we set α e for our algorithms. |