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..
Resilient Multi-Agent Reinforcement Learning with Adversarial Value Decomposition
Authors: Thomy Phan, Lenz Belzner, Thomas Gabor, Andreas Sedlmeier, Fabian Ritz, Claudia Linnhoff-Popien11308-11316
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate RADAR in two cooperative multi-agent domains and show that RADAR achieves better worst case performance w.r.t. arbitrary agent changes than state-of-the-art MARL. An empirical evaluation of RADAR in two cooperative multi-agent domains and a comparison with state-of-the-art MARL w.r.t. the proposed test scheme. |
| Researcher Affiliation | Collaboration | Thomy Phan,1 Lenz Belzner,2 Thomas Gabor,1 Andreas Sedlmeier,1 Fabian Ritz,1 Claudia Linnhoff-Popien1 1LMU Munich, 2Maiborn Wolff thomy.phan@iļ¬.lmu.de |
| Pseudocode | Yes | Algorithm 1 Randomized Adversarial Training (RAT) |
| Open Source Code | Yes | Code available at https://github.com/thomyphan/resilient-marl |
| Open Datasets | No | We implemented a predator-prey (PP) and a cyber-physical production system (CPPS) domain with N agents. The paper describes custom environments/domains that were implemented for the experiments, but does not provide concrete access information (link, DOI, formal citation) for a publicly available dataset. |
| Dataset Splits | No | The paper discusses training runs and testing, but does not specify explicit train/validation/test dataset splits with percentages or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'ADAM' as an optimizer but does not specify versions for any key software components or libraries. |
| Experiment Setup | Yes | The neural networks are updated every 1000 time steps using ADAM with a learning rate of 0.001. We set Ī³ = 0.95, T = 4000, and Ne = 10 (Algorithm 1). |