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..
Multi-Agent Common Knowledge Reinforcement Learning
Authors: Christian Schroeder de Witt, Jakob Foerster, Gregory Farquhar, Philip Torr, Wendelin Boehmer, Shimon Whiteson
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Pairwise MACKRL (henceforth referred to as MACKRL) on two environments3 : ο¬rst, we use a matrix game with special coordination requirements to illustrate MACKRL s ability to surpass both IL and JAL. Secondly, we employ MACKRL with deep recurrent neural network policies in order to outperform state-of-the-art baselines on a number of challenging Star Craft II unit micromanagement tasks. |
| Researcher Affiliation | Academia | Correspondence to Christian Schroeder de Witt <EMAIL> University of Oxford, UK |
| Pseudocode | Yes | Algorithm 1 Decentralised action selection for agent a 2 A in MACKRL; Algorithm 2 Compute joint policies for a given u G env of a group of agents G in MACKRL |
| Open Source Code | Yes | All source code is available at https://github.com/schroederdewitt/mackrl. |
| Open Datasets | Yes | We then apply MACKRL to challenging Star Craft II unit micromanagement tasks (Vinyals et al., 2017) from the Star Craft Multi-Agent Challenge (SMAC, Samvelyan et al., 2019). |
| Dataset Splits | No | All experiments use SMAC settings for comparability (see Samvelyan et al. (2019) and Appendix B for details). |
| Hardware Specification | No | It was also supported by the Oxford-Google Deep Mind Graduate Scholarship and a generous equipment grant from NVIDIA. |
| Software Dependencies | No | No specific software versions or dependencies with version numbers are provided in the paper. |
| Experiment Setup | No | All experiments use SMAC settings for comparability (see Samvelyan et al. (2019) and Appendix B for details). In addition, MACKRL and its within-class baseline Central-V share equal hyper-parameters as far as applicable. |