HyperAI超神经

Smac On Smac Def Outnumbered Sequential

评估指标

Median Win Rate

评测结果

各个模型在此基准测试上的表现结果

		Paper Title
DRIMA	100	Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
QTRAN	81.3	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
MADDPG	81.3	Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
VDN	15.6	Value-Decomposition Networks For Cooperative Multi-Agent Learning
DDN	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DMIX	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
MASAC	0.0	Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
IQL	0.0	The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
COMA	0.0	Counterfactual Multi-Agent Policy Gradients
QMIX	0.0	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
DIQL	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

0 of 11 row(s) selected.

HyperAI超神经

Smac On Smac Def Outnumbered Sequential

评估指标

Median Win Rate

评测结果

各个模型在此基准测试上的表现结果

		Paper Title
DRIMA	100	Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
QTRAN	81.3	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
MADDPG	81.3	Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
VDN	15.6	Value-Decomposition Networks For Cooperative Multi-Agent Learning
DDN	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DMIX	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
MASAC	0.0	Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
IQL	0.0	The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
COMA	0.0	Counterfactual Multi-Agent Policy Gradients
QMIX	0.0	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
DIQL	0.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

0 of 11 row(s) selected.

Smac On Smac Def Outnumbered Sequential | SOTA | HyperAI超神经