Smac On Smac Def Armored Sequential

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Median Win Rate

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
DRIMA	100	Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
VDN	96.9	Value-Decomposition Networks For Cooperative Multi-Agent Learning
QTRAN	93.8	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
MADDPG	90.6	Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
DMIX	81.3	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DDN	71.9	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DIQL	53.1	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL	9.4	The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
QMIX	0.0	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
MASAC	0.0	Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
COMA	0.0	Counterfactual Multi-Agent Policy Gradients

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Smac On Smac Def Armored Sequential | SOTA | HyperAI