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Smac On Smac Off Superhard Parallel

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Median Win Rate

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Median Win Rate	Paper Title	Repository
VDN	0.0	Value-Decomposition Networks For Cooperative Multi-Agent Learning
DRIMA	0.0	Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning	-
DDN	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DIQL	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
COMA	0.0	Counterfactual Multi-Agent Policy Gradients
DMIX	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL	0.0	The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
MASAC	0.0	Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
QMIX	0.0	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
QTRAN	0.0	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

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