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Smac On Smac Off Near Parallel

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Median Win Rate

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Median Win Rate	Paper Title	Repository
QMIX	95.0	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
DIQL	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
COMA	20.0	Counterfactual Multi-Agent Policy Gradients
DDN	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QTRAN	0.0	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
IQL	5.0	-	-
DMIX	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
MASAC	0.0	Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
VDN	90.0	Value-Decomposition Networks For Cooperative Multi-Agent Learning
DRIMA	95.0	Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning	-

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