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..
Heterogeneous Multi-Agent Bandits with Parsimonious Hints
Authors: Amirmahdi Mirfakhar, Xuchuang Wang, Jinhang Zuo, Yair Zick, Mohammad Hajiesmaili
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we establish lower bounds to prove the optimality of our results and verify them through numerical simulations. We executed the algorithms HCLA, GP-HCLA, G-HCLA, HD-ETC, and EBHD-ETC with M = 4, K = 4, and match min 0.18, averaging regret and hint complexity over 50 replications for 105 rounds. |
| Researcher Affiliation | Academia | 1University of Massachusetts Amherst, 2City University of Hong Kong EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Hinted Centralized Learning Algorithm (HCLA) Algorithm 2: Generalized Projection-based Hinted Centralized Learning Algorithm (GP-HCLA) Algorithm 3: Hinted Decentralized Explore then Commit (HD-ETC) : agent m Algorithm 4: Elimination-Based Hinted Decentralized Explore then Commit (EBHD-ETC) : agent m |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of source code for the described methodology. |
| Open Datasets | No | We executed the algorithms HCLA, GP-HCLA, G-HCLA, HD-ETC, and EBHD-ETC with M = 4, K = 4, and match min 0.18, averaging regret and hint complexity over 50 replications for 105 rounds. The experiments appear to be based on simulated environments rather than a specific external dataset, and no dataset is mentioned as publicly available. |
| Dataset Splits | No | The paper does not use an external dataset, instead, it describes numerical simulations based on specified parameters (M, K, match min), so there are no dataset splits to provide. |
| Hardware Specification | No | The paper mentions numerical simulations and experiments, but does not provide any specific details about the hardware used to run these experiments. |
| Software Dependencies | No | The paper describes algorithms and numerical simulations but does not specify any software dependencies or their version numbers used for implementation or experimentation. |
| Experiment Setup | Yes | We executed the algorithms HCLA, GP-HCLA, G-HCLA, HD-ETC, and EBHD-ETC with M = 4, K = 4, and match min 0.18, averaging regret and hint complexity over 50 replications for 105 rounds. |