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Smac On Smac Mmm2 1

Métriques

Median Win Rate

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Median Win Rate
Paper TitleRepository
ACE100ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
QMIX69The StarCraft Multi-Agent Challenge
DIQL85.23DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DPLEX96.88A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
QMIX69Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
IQL0The StarCraft Multi-Agent Challenge
VDN1The StarCraft Multi-Agent Challenge
QMIX92.44DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
VDN89.2DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL68.92DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
Heuristic0The StarCraft Multi-Agent Challenge
DDN97.22DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DMIX95.11DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QPLEX96.88A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
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