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..
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
Authors: Zhaoxing Yang, Haiming Jin, Rong Ding, Haoyi You, Guiyun Fan, Xinbing Wang, Chenghu Zhou
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct extensive experiments to show the effectiveness of De COM with various types of costs in both moderate-scale and large-scale (with 500 agents) environments that originate from real-world applications. |
| Researcher Affiliation | Academia | Shanghai Jiao Tong University, Shanghai, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Training Algorithm of De COM |
| Open Source Code | No | The paper does not provide an explicit statement of code release or a link to a source code repository for the methodology described. |
| Open Datasets | Yes | CLFM is built with a public city-scale dataset7 that contains approximate 1 million orders from November 1 to November 30, 2016 in Chengdu, China. 7Data source: Di Di Chuxing GAIA Open Dataset Initiative (https://gaia.didichuxing.com). |
| Dataset Splits | No | The paper mentions using datasets for experiments but does not provide specific training/validation/test dataset splits (percentages, counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'MADDPG critics' and 'Mean-Field critics' but does not provide specific version numbers for any software dependencies, libraries, or solvers. |
| Experiment Setup | Yes | We set λ in CTC-safe, CDSN and CLFM as 1, and 0.01 in CTC-fair. Due to space limit, we put more discussions about chooing λ and detailed training curves in Appendix B.5. |