Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
Authors: Lebin Yu, Yunbo Qiu, Quanming Yao, Yuan Shen, Xudong Zhang, Jian Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |