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Smac On Smac Off Distant Parallel

Metrics

Median Win Rate

Results

Performance results of various models on this benchmark

Model Name
Median Win Rate
Paper TitleRepository
DIQL0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
DDN0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
IQL0.0--
QMIX0.0QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning-
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-
QTRAN0.0QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning-
VDN85.0Value-Decomposition Networks For Cooperative Multi-Agent Learning-
COMA0.0Counterfactual Multi-Agent Policy Gradients-
DRIMA95.0Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning-
0 of 10 row(s) selected.
Smac On Smac Off Distant Parallel | SOTA | HyperAI