Smac On Smac Def Infantry 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
DMIX	100	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
MADDPG	100	Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
QTRAN	100	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
QMIX	96.9	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
VDN	96.9	Value-Decomposition Networks For Cooperative Multi-Agent Learning
DIQL	93.8	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL	93.8	The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
DDN	90.6	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
MASAC	37.5	Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
COMA	28.1	Counterfactual Multi-Agent Policy Gradients

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Smac On Smac Def Infantry Sequential | SOTA | HyperAI