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..
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
Authors: Lebin Yu, Yunbo Qiu, Quanming Yao, Yuan Shen, Xudong Zhang, Jian Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The superiority of ADMAC over existing methods is validated by experiments in three communication-critical tasks under four types of attacks. |
| Researcher Affiliation | Academia | Department of Electronic Engineering, BNRist, Tsinghua University, Beijing 100084, China |
| Pseudocode | No | The provided paper does not contain explicit pseudocode or algorithm blocks in its main content. |
| Open Source Code | No | The paper does not contain an explicit statement or link confirming the public availability of the source code for the methodology described. |
| Open Datasets | Yes | We implement three communication-critical multi-agent environments for demonstrative purposes: Food Collector (Sun et al. 2023), Predator Prey (Singh, Jain, and Sukhbaatar 2018), and Treasure Hunt (Freed et al. 2020). |
| Dataset Splits | No | The paper describes how the dataset for the reliability estimator is generated, but it does not specify explicit training, validation, and test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | We set σ = 0.5 in the experiments. ... we set η = 1 to obtain a strong attack. ... We set ϵ = 0.3 and use 5-step updates to obtain the final perturbed messages. |