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Smac On Smac Def Armored Sequential

Metrics

Median Win Rate

Results

Performance results of various models on this benchmark

Model Name
Median Win Rate
Paper TitleRepository
QTRAN93.8QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning-
VDN96.9Value-Decomposition Networks For Cooperative Multi-Agent Learning-
QMIX0.0QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning-
MASAC0.0Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning-
DRIMA100Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning-
IQL9.4The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions-
DDN71.9DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
COMA0.0Counterfactual Multi-Agent Policy Gradients-
MADDPG90.6Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments-
DIQL53.1DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
DMIX81.3DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
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Smac On Smac Def Armored Sequential | SOTA | HyperAI