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Smac On Smac Off Near Parallel

评估指标

Median Win Rate

评测结果

各个模型在此基准测试上的表现结果

模型名称
Median Win Rate
Paper TitleRepository
QMIX95.0QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning-
DIQL0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
COMA20.0Counterfactual Multi-Agent Policy Gradients-
DDN0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
QTRAN0.0QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning-
IQL5.0--
DMIX0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
MASAC0.0Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning-
VDN90.0Value-Decomposition Networks For Cooperative Multi-Agent Learning-
DRIMA95.0Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning-
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