Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Perturbed-History Exploration in Stochastic Multi-Armed Bandits
Authors: Branislav Kveton, Csaba Szepesvári, Mohammad Ghavamzadeh, Craig Boutilier
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate PHE and show that it is competitive with state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | Branislav Kveton1 , Csaba Szepesv ari2,3 , Mohammad Ghavamzadeh4 and Craig Boutilier1 1Google Research 2Deep Mind 3 University of Alberta 4Facebook AI Research |
| Pseudocode | Yes | Algorithm 1 Perturbed-history exploration in a multi-armed bandit with [0, 1] rewards |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating '100 randomly chosen problems in each class' (Bernoulli and Beta bandit problems) and references 'Kveton et al. [2019b]' for the problem classes, but does not provide access information for a specific, pre-existing public dataset. |
| Dataset Splits | No | The paper describes an online learning problem (multi-armed bandits) and measures regret over 'n' rounds, which means there is no traditional train/validation/test dataset split. |
| Hardware Specification | No | The paper provides run times in its experimental section but does not specify any hardware details such as GPU/CPU models or other system specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | We experiment with three settings of perturbation scales a in PHE: 2.1, 1.1, and 0.5. ... We experiment with 100 randomly chosen problems in each class. Each problem has K = 10 arms and the mean rewards of these arms are chosen uniformly at random from interval [0.25, 0.75]. The horizon is n = 10000 rounds. |