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SotA/Methodology/Multi-agent Reinforcement Learning/SMAC 3s5z_vs_3s6z

Multi-agent Reinforcement Learning on SMAC 3s5z_vs_3s6z

Metric: Median Win Rate (higher is better)

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#Model↕Median Win Rate▼AugmentationsPaperDate↕Code
1ACE100NoACE: Cooperative Multi-agent Q-learning with Bid...2022-11-29Code
2DDN94.03NoDFAC Framework: Factorizing the Value Function v...2021-02-16Code
3DMIX91.08NoDFAC Framework: Factorizing the Value Function v...2021-02-16Code
4DPLEX90.62NoA Unified Framework for Factorizing Distribution...2023-06-04Code
5VDN89.2NoDFAC Framework: Factorizing the Value Function v...2021-02-16Code
6QPLEX84.38NoA Unified Framework for Factorizing Distribution...2023-06-04Code
7QMIX67.22NoDFAC Framework: Factorizing the Value Function v...2021-02-16Code
8DIQL62.22NoDFAC Framework: Factorizing the Value Function v...2021-02-16Code
9IQL29.83NoDFAC Framework: Factorizing the Value Function v...2021-02-16Code
10VDN2NoThe StarCraft Multi-Agent Challenge2019-02-11Code
11QMIX2NoMonotonic Value Function Factorisation for Deep ...2020-03-19Code
12IQL0No--Code
13Heuristic0No--Code