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SotA/Methodology/Multi-agent Reinforcement Learning/SMAC 6h_vs_8z

Multi-agent Reinforcement Learning on SMAC 6h_vs_8z

Metric: Median Win Rate (higher is better)

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#Model↕Median Win Rate▼AugmentationsPaperDate↕Code
1ACE93.75NoACE: Cooperative Multi-agent Q-learning with Bid...2022-11-29Code
2DDN83.92NoDFAC Framework: Factorizing the Value Function v...2021-02-16Code
3DMIX49.43NoDFAC Framework: Factorizing the Value Function v...2021-02-16Code
4DPLEX43.75NoA Unified Framework for Factorizing Distribution...2023-06-04Code
5QMIX12.78NoDFAC Framework: Factorizing the Value Function v...2021-02-16Code
6QMIX3NoMonotonic Value Function Factorisation for Deep ...2020-03-19Code
7QMIX3NoMonotonic Value Function Factorisation for Deep ...2020-03-19Code
8VDN0No--Code
9DIQL0No--Code
10IQL0No--Code
11IQL0No--Code
12VDN0No--Code
13Heuristic0No--Code