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..
Multi-Agent Multi-Armed Bandits with Limited Communication
Authors: Mridul Agarwal, Vaneet Aggarwal, Kamyar Azizzadenesheli
JMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider various problem setups to evaluate our algorithms. We compare the proposed algorithms, LCC-UCB and LCC-UCB-GRAPH with a no-communication strategy and a full communication strategy. ... We present the result in Fig. 2 for 30 independent runs for expected rewards drawn from uniform U(0, 1) distribution. |
| Researcher Affiliation | Academia | Mridul Agarwal EMAIL Vaneet Aggarwal EMAIL Kamyar Azizzadenesheli EMAIL Purdue University |
| Pseudocode | Yes | Algorithm 1 LCC-UCB(n, Sn, [N] \ {n}, T) ... Algorithm 2 UCB(n, A, Tj) ... Algorithm 3 LCC-UCB-GRAPH(Sn, G, T0, T) |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes simulations using expected rewards drawn from a uniform U(0,1) distribution and generated Erdős-Rényi graphs, but does not provide concrete access information (link, DOI, repository, or citation) for any pre-existing publicly available dataset. |
| Dataset Splits | No | The paper describes generating data for simulations and conducting '30 independent runs,' but does not specify dataset splits (e.g., training, validation, test percentages or counts) as it does not use a fixed, pre-existing dataset. |
| Hardware Specification | No | The paper discusses experimental evaluations and simulations but does not provide specific details about the hardware used, such as GPU/CPU models, processors, or memory specifications. |
| Software Dependencies | No | The paper does not explicitly list any specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their versions) that would be needed to replicate the experiments. |
| Experiment Setup | Yes | We consider a horizon of T = 105 steps. We study the behaviour of the algorithm by varying the number of agents N and the number of arms K. We choose three pairs (N, K), which are (10, 100), (20, 100), (10, 200). We present the result in Fig. 2 for 30 independent runs for expected rewards drawn from uniform U(0, 1) distribution. ... We specifically consider Erd os-R enyi graphs G(N, p) where N >= 100 vertices are a swarm of N agents. Also, p = 10/N ln N/N is the edge selection probability. ... We again consider 3 cases of (N, K) which are (100, 250), (150, 250), and (100, 500). We present the result in Fig. 4 for 30 independent runs. |