Papers › QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

30 Mar 2018ICML 2018 7arXiv:1803.11485archive 2025-07-28

Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson

In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted. Learning joint action-values conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralised policies is unclear. Our solution is QMIX, a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations. We structurally enforce that the joint-action value is monotonic in the per-agent values, which allows tractable maximisation of the joint action-value in off-policy learning, and guarantees consistency between the centralised and decentralised policies. We evaluate QMIX on a challenging set of StarCraft II micromanagement tasks, and show that QMIX significantly outperforms existing value-based multi-agent reinforcement learning methods.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="1803.11485")

Code

Syntology Ran 9 of 11 code samples harvested from 7 repositories linked to this paper; 2 have no recorded run. Of those that ran: 1 ran · fixture could not drive it; 8 ran with no contract checked.

By repository: community (archive-listed): 11 samples from 7 repositories, 9 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

18 repositories listed; official and paper-mentioned ones first.

oxwhirl/pymarl officialmentioned on GitHubpytorch report
TonghanWang/DOP mentioned on GitHubpytorch report
TonghanWang/NDQ mentioned on GitHubpytorch report
cathyhxh/ctds mentioned on GitHubpytorch report
facebookresearch/benchmarl mentioned on GitHubpytorchMIT report
gingkg/smac mentioned on GitHubpytorchMIT report
hhhusiyi-monash/UPDeT mentioned on GitHubpytorch report
ifpen/wfcrl-benchmark mentioned on GitHubpytorch report
jugg1er/air mentioned on GitHubpytorchApache-2.0 report
nju-rl/acorm mentioned on GitHubpytorch report
oxwhirl/smac mentioned on GitHubpytorchMIT report
puyuan1996/MARL mentioned on GitHubpytorch report
starry-sky6688/marl-algorithms mentioned on GitHubpytorch report
15534081591/QMIX mindsporeApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

11 samples harvested; 9 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · fixture could not drive it
8ran
2unverified

Licence: 6 of the 11 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 7 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

QMIX starry-sky6688/marl-algorithms/policy/qmix.py community (archive-listed) ran no licence file found · pointer only · 8d9a70332f1c598e · report
QMIX puyuan1996/MARL/src/policy/qmix.py community (archive-listed) ran no licence file found · pointer only · 31909679695a8aef · report
QMIX_Net nju-rl/acorm/ACORM_QMIX/algorithm/vdn_qmix.py community (archive-listed) ran no licence file found · pointer only · 44128055a06128d8 · report
QMixNet starry-sky6688/marl-algorithms/policy/qmix.py community (archive-listed) ran · metamorphic tier: deterministic no licence file found · pointer only · 48fb98cde08d9bcf · report
QMixer TonghanWang/NDQ/src/modules/mixers/qmix.py community (archive-listed) ran · metamorphic tier: deterministic Apache-2.0 (permissive) · d8b2521ad77f9ed0 · report
QMixer TonghanWang/DOP/src/modules/mixers/qmix.py community (archive-listed) ran Apache-2.0 (permissive) · dbba778166e49f16 · report
QMixer ifpen/wfcrl-benchmark/algos/baseline_qmix.py community (archive-listed) ran · metamorphic tier: invariant Apache-2.0 (permissive) · 7e8ae107f35df50f · report
RNN starry-sky6688/marl-algorithms/policy/qmix.py community (archive-listed) ran · metamorphic tier: deterministic no licence file found · pointer only · 9e0f6a55ec27b590 · report
smooth nju-rl/acorm/plot.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · 30cf9bec8ce2ebf3 · report
build_inputs 15534081591/QMIX/src/run_310.py community (archive-listed) unverified Apache-2.0 (permissive) · a9ef2f2d8fcf96f3 · report
select_actions 15534081591/QMIX/src/run_310.py community (archive-listed) unverified Apache-2.0 (permissive) · fc58bf6a73461826 · report

Tasks

Multi-agent Reinforcement LearningReinforcement LearningReinforcement Learning (RL)SMAC+StarcraftStarcraft IIreinforcement-learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
SMAC+ Def_Armored_parallel QMIX Median Win Rate 75.0 #2 of 10 Archive leaderboard report
SMAC+ Def_Armored_sequential QMIX Median Win Rate 0.0 #10 of 11 Archive leaderboard report
SMAC+ Def_Infantry_parallel QMIX Median Win Rate 95.0 #3 of 10 Archive leaderboard report
SMAC+ Def_Infantry_sequential QMIX Median Win Rate 96.9 #5 of 11 Archive leaderboard report
SMAC+ Def_Outnumbered_parallel QMIX Median Win Rate 30.0 #2 of 10 Archive leaderboard report
SMAC+ Def_Outnumbered_sequential QMIX Median Win Rate 0.0 #6 of 11 Archive leaderboard report
SMAC+ Off_Complicated_parallel QMIX Median Win Rate 0.0 #7 of 10 Archive leaderboard report
SMAC+ Off_Complicated_sequential QMIX Median Win Rate 87.5 #2 of 4 Archive leaderboard report
SMAC+ Off_Distant_parallel QMIX Median Win Rate 0.0 #7 of 10 Archive leaderboard report
SMAC+ Off_Distant_sequential QMIX Median Win Rate 93.8 #2 of 4 Archive leaderboard report
SMAC+ Off_Hard_parallel QMIX Median Win Rate 0.0 #7 of 10 Archive leaderboard report
SMAC+ Off_Hard_sequential QMIX Median Win Rate 96.9 #1 of 4 Archive leaderboard report
SMAC+ Off_Near_parallel QMIX Median Win Rate 95.0 #1 of 10 Archive leaderboard report
SMAC+ Off_Near_sequential QMIX Median Win Rate 90.6 #2 of 4 Archive leaderboard report
SMAC+ Off_Superhard_parallel QMIX Median Win Rate 0.0 #5 of 10 Archive leaderboard report
SMAC+ Off_Superhard_sequential QMIX Median Win Rate 0.0 #3 of 4 Archive leaderboard report
Starcraft II SMAC-Exp QMIX Median Win Rate % #1 of 1 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections