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Smac On Smac Def Infantry Parallel

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Median Win Rate

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Median Win Rate	Paper Title	Repository
DIQL	45.0	DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
DMIX	90.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL	40.0	The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
COMA	50.0	Counterfactual Multi-Agent Policy Gradients
DDN	20.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QTRAN	100.0	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
DRIMA	100.0	Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning	-
QMIX	95.0	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
MASAC	30.0	Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
VDN	95.0	Value-Decomposition Networks For Cooperative Multi-Agent Learning

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