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Smac On Smac Off Near Parallel

Metrics

Median Win Rate

Results

Performance results of various models on this benchmark

Model Name
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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Smac On Smac Off Near Parallel | SOTA | HyperAI