HyperAIHyperAI초신경
홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
전체 검색
소개
HyperAIHyperAI초신경

Smac On Smac Def Outnumbered Sequential

평가 지표

Median Win Rate

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
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.