HyperAI초신경
홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
전체 검색
소개
HyperAI초신경

Smac On Smac Mmm2 1

평가 지표

Median Win Rate

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Median Win Rate
Paper TitleRepository
ACE100ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
QMIX69The StarCraft Multi-Agent Challenge
DIQL85.23DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DPLEX96.88A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
QMIX69Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
IQL0The StarCraft Multi-Agent Challenge
VDN1The StarCraft Multi-Agent Challenge
QMIX92.44DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
VDN89.2DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL68.92DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
Heuristic0The StarCraft Multi-Agent Challenge
DDN97.22DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DMIX95.11DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QPLEX96.88A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
0 of 14 row(s) selected.