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Smac On Smac Def Armored Parallel

Métriques

Median Win Rate

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Median Win Rate
Paper TitleRepository
DMIX90.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
COMA0.0Counterfactual Multi-Agent Policy Gradients
DDN0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QMIX75.0QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
DRIMA60.0Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning-
DIQL0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL0.0The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
MASAC0.0Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
QTRAN5.0QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
VDN5.0Value-Decomposition Networks For Cooperative Multi-Agent Learning
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Smac On Smac Def Armored Parallel | SOTA | HyperAI