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..
Distributed Multi-Player Bandits - a Game of Thrones Approach
Authors: Ilai Bistritz, Amir Leshem
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We simulated a multi-armed bandit game with {µn,i} that are chosen independently and uniformly at random in [0.05, 0.95]. The rewards are generated as rn,i (t) = µn,i + zn,i (t) where {zn,i (t)} are independent and uniformly distributed on [ 0.05, 0.05] for each n, i. In Fig. 2, we present the sample mean of the accumulated sum of utilities PN n=1 1 t Pt τ=1 un (a (τ)) as a function of time t, averaged over 100 experiments. |
| Researcher Affiliation | Academia | Ilai Bistritz Stanford University EMAIL Amir Leshem Bar Ilan University EMAIL |
| Pseudocode | Yes | Algorithm 1 Game of Thrones Algorithm and Algorithm 2 Game of Thrones Dynamics |
| Open Source Code | No | The paper does not provide any explicit statements about the release of open-source code, nor does it include a link to a code repository. |
| Open Datasets | No | The paper describes a simulated environment where data is generated for experiments rather than using or providing a publicly available dataset. It states: 'We simulated a multi-armed bandit game with {µn,i} that are chosen independently and uniformly at random in [0.05, 0.95]. The rewards are generated as rn,i (t) = µn,i + zn,i (t) where {zn,i (t)} are independent and uniformly distributed on [ 0.05, 0.05] for each n, i.' |
| Dataset Splits | No | The paper does not mention train/validation/test dataset splits. It describes an online learning framework with 'exploration', 'Game of Thrones (Go T)', and 'exploitation' phases within its simulation. |
| Hardware Specification | No | The paper describes its simulations ('We simulated a multi-armed bandit game...') but does not provide any specific details about the hardware (e.g., CPU, GPU, memory) used to conduct these simulations. |
| Software Dependencies | No | The paper describes numerical simulations but does not specify any software dependencies or their version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | Hence we choose c1 = 1000, c2 = c3 = 6000. We use ρ = 1/2 in the simulations we present, since the performance is very similar for ρ values not too close to zero or one. We use c = N, that gives the highest possible escape probability of εc from a content state. |