Decentralized Exploration in Multi-Armed Bandits
Authors: Raphael Feraud, Reda Alami, Romain Laroche
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate and complete the analysis of DECENTRALIZED ELIMINATION, we run three synthetic experiments: (...) The algorithms are compared with respect to two key performance indicators: the sample complexity and the communication cost. For all the experiments, ϵ is set to 0.25, and δ is set to 0.05. The privacy level η is set to 0.9. All the curves and the measures are averaged over 20 trials. |
| Researcher Affiliation | Industry | 1Orange Labs 2Microsoft Research. |
| Pseudocode | Yes | Algorithm 1 DECENTRALIZED EXPLORATION PROBLEM (...) Algorithm 2 DECENTRALIZED ELIMINATION |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the described methodology. |
| Open Datasets | No | The paper uses synthetic experiments ('Problem 1: Uniform distribution of players', 'Problem 2: 50% of players generates 80% of events', 'Problem 3: non-stationary rewards') rather than external public datasets, and provides no access information or citations for any dataset. |
| Dataset Splits | No | The paper mentions running experiments over '20 trials' but does not specify any training, validation, or test dataset splits or a methodology for data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using algorithms like UGAPEC and SER3, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | For all the experiments, ϵ is set to 0.25, and δ is set to 0.05. The privacy level η is set to 0.9. |