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Smac On Smac Mmm2 1

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Median Win Rate

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
Median Win Rate
Paper TitleRepository
ACE100ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency-
QMIX69The StarCraft Multi-Agent Challenge-
DIQL85.23DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
DPLEX96.88A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning-
QMIX69Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning-
IQL0The StarCraft Multi-Agent Challenge-
VDN1The StarCraft Multi-Agent Challenge-
QMIX92.44DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
VDN89.2DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
IQL68.92DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
Heuristic0The StarCraft Multi-Agent Challenge-
DDN97.22DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
DMIX95.11DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
QPLEX96.88A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning-
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Smac On Smac Mmm2 1 | SOTA | HyperAI