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Smac On Smac Corridor

Metrics

Average Score

Median Win Rate

Results

Performance results of various models on this benchmark

			Paper Title	Repository
ACE	-	100	ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
DDN	20	95.4	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DIQL	19.68	91.62	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DMIX	19.66	90.45	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
VDN	19.47	85.34	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL	19.42	84.87	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DPLEX	19.08	81.25	A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
QPLEX	18.73	75.00	A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
QMIX	15.07	37.61	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QMIX	-	1	Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
QMIX	-	1	The StarCraft Multi-Agent Challenge
IQL	-	0	The StarCraft Multi-Agent Challenge
Heuristic	-	0	The StarCraft Multi-Agent Challenge

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Smac On Smac Corridor | SOTA | HyperAI