HyperAIHyperAI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SOTALLMモデルGPU ランキング学会
検索
サイトについて
HyperAIHyperAI超神経

Smac On Smac Def Outnumbered 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 TitleRepository
DDN0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DMIX0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
MASAC0.0Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
VDN15.6Value-Decomposition Networks For Cooperative Multi-Agent Learning
DRIMA100Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning-
IQL0.0The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
COMA0.0Counterfactual Multi-Agent Policy Gradients
QTRAN81.3QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
QMIX0.0QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
MADDPG81.3Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
DIQL0.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
0 of 11 row(s) selected.