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..
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Authors: Sai Qian Zhang, Qi Zhang, Jieyu Lin
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation using multiple MARL benchmarks indicates that our method achieves 2 10 lower in communication overhead than state-of-the-art MARL algorithms, while allowing agents to achieve better overall performance. |
| Researcher Affiliation | Collaboration | Sai Qian Zhang Harvard University Qi Zhang Amazon Inc. Jieyu Lin University of Toronto |
| Pseudocode | Yes | Algorithm 1: Communication protocol at agent i |
| Open Source Code | Yes | The code is available at https://github.com/saizhang0218/VBC. |
| Open Datasets | Yes | For evaluation, we test VBC on several MARL benchmarks, including Star Craft Multi-Agent Challenge [15], Cooperative Navigation (CN) [10] and Predator-prey (PP) [8]. |
| Dataset Splits | No | The paper describes training duration (e.g., '2 million and 4 million episodes') and test episodes ('20 test episodes'), but does not explicitly specify train/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For hyperparameters used by VBC (i.e., λ used in equation (1), δ1andδ2 in Algorithm 1), we first search for a coarse parameter range based on random trial, experience and message statistics. We then perform a random search within a smaller hyperparameter space. Best selections are shown in the legend of each figure. |