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Smac On Smac 6H Vs 8Z 1

Metrics

Median Win Rate

Results

Performance results of various models on this benchmark

		Paper Title	Repository
ACE	93.75	ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
DDN	83.92	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DMIX	49.43	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DPLEX	43.75	A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
QMIX	12.78	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QMIX	3	Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
QMIX	3	The StarCraft Multi-Agent Challenge
IQL	0	The StarCraft Multi-Agent Challenge
VDN	0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
Heuristic	0	The StarCraft Multi-Agent Challenge
VDN	0	The StarCraft Multi-Agent Challenge
IQL	0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DIQL	0.00	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QPLEX	-	A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

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Smac On Smac 6H Vs 8Z 1 | SOTA | HyperAI