Datasets › SMAC-Exp
SMAC-Exp (StarCraft Multi-Agent Exploration Challenge)
The StarCraft Multi-Agent Challenges+ requires agents to learn completion of multi-stage tasks and usage of environmental factors without precise reward functions. The previous challenges (SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning are mainly concerned with ensuring that all agents cooperatively eliminate approaching adversaries only through fine manipulation with obvious reward functions. This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control. This study covers both offensive and defensive scenarios. In the offensive scenarios, agents must learn to first find opponents and then eliminate them. The defensive scenarios require agents to use topographic features. For example, agents need to position themselves behind protective structures to make it harder for enemies to attack.
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Multi-agent Reinforcement Learning | SMAC-Exp | DRIMA Median Win Rate 15 | Neural Processes with Stochastic Attention: Paying more... | mingyukim87/npwsa | 1 | Compare |
| Starcraft II | SMAC-Exp | QMIX Median Win Rate % | QMIX: Monotonic Value Function Factorisation for Deep... | ray-project/ray +17 | 1 | Compare |
Papers archive 2025-07-28
2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 11. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Neural Processes with Stochastic Attention: Paying more attention to the context dataset | 1 | 1 | 11 Apr 2022 | not harvested |
| QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning | 18 | 1 | 30 Mar 2018 | ran 9 of 11 samples (2 unverified; 6 pointer-only for licence) |
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
MIT
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- Off_Superhard_sequential
- Def_Outnumbered_sequential
- Def_Armored_sequential
- Off_Hard_parallel
- Off_Superhard_parallel
- Off_Complicated_parallel
- Def_Outnumbered_parallel
- Off_Distant_parallel
- Off_Near_parallel
- Def_Armored_parallel
- Def_Infantry_parallel
- SMAC-Exp
12 variant names, as the archive lists them.
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