HyperAIHyperAI超神经
首页资讯最新论文教程数据集百科SOTALLM 模型天梯GPU 天梯顶会
全站搜索
关于
HyperAIHyperAI超神经

Smac On Smac Def Outnumbered Parallel

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

模型名称
Median Win Rate
Paper TitleRepository
DIQL0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
QMIX30.0QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning-
IQL0.0The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions-
DRIMA70.0Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning-
DMIX5.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
COMA0.0Counterfactual Multi-Agent Policy Gradients-
MASAC0.0Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning-
VDN0.0Value-Decomposition Networks For Cooperative Multi-Agent Learning-
DDN0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
QTRAN0.0QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning-
0 of 10 row(s) selected.