HyperAI超神经

首页资讯最新论文教程数据集百科 SOTA LLM 模型天梯 GPU 天梯顶会

中文

HyperAI超神经

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

0 of 10 row(s) selected.

Smac On Smac Off Superhard Parallel | SOTA | HyperAI超神经