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..
DM²: Decentralized Multi-Agent Reinforcement Learning via Distribution Matching
Authors: Caroline Wang, Ishan Durugkar, Elad Liebman, Peter Stone
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental validation on the Star Craft domain shows that combining (1) a task reward, and (2) a distribution matching reward for expert demonstrations for the same task, allows agents to outperform a naive distributed baseline. Additional experiments probe the conditions under which expert demonstrations need to be sampled to obtain the learning benefits. |
| Researcher Affiliation | Collaboration | 1 The University of Texas at Austin 2 Spark Cognition Research 3 Sony AI EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: DM2 (Decentralized MARL via distribution matching) |
| Open Source Code | Yes | The code is provided at https://github.com/carolinewang01/dm2. |
| Open Datasets | Yes | Experiments were conducted on the Star Craft Multi-Agent Challenge domain (Samvelyan et al. 2019). |
| Dataset Splits | No | The paper mentions evaluating on test episodes and using demonstration data, but does not specify explicit training/validation/test dataset splits (e.g., percentages or counts) for their main learning process. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions using PPO, QMIX, RMAPPO, and GAIL, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Experimental details such as hyperparameters are specified in Appendix C. |