HyperAI超神経

ホームニュース最新論文チュートリアルデータセット百科事典 SOTA LLMモデル GPU ランキング学会

サイトについて

日本語

HyperAI超神経

Smac On Smac Def Infantry Sequential

評価指標

Median Win Rate

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名	Median Win Rate	Paper Title	Repository
DRIMA	100	Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning	-
DIQL	93.8	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DDN	90.6	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DMIX	100	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
MADDPG	100	Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
QTRAN	100	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
IQL	93.8	The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
QMIX	96.9	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
COMA	28.1	Counterfactual Multi-Agent Policy Gradients
MASAC	37.5	Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
VDN	96.9	Value-Decomposition Networks For Cooperative Multi-Agent Learning

0 of 11 row(s) selected.