Startseite Neuigkeiten Neueste Forschungsarbeiten Tutorials Datensätze Wiki SOTA LLM-Modelle GPU-Rangliste Veranstaltungen

Deutsch

Smac On Smac Corridor

Metriken

Average Score

Median Win Rate

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Average Score	Median Win Rate	Paper Title	Repository
DIQL	19.68	91.62	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DPLEX	19.08	81.25	A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
VDN	19.47	85.34	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL	-	0	The StarCraft Multi-Agent Challenge
DDN	20	95.4	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
Heuristic	-	0	The StarCraft Multi-Agent Challenge
QMIX	15.07	37.61	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DMIX	19.66	90.45	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QPLEX	18.73	75.00	A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
ACE	-	100	ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
QMIX	-	1	Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
QMIX	-	1	The StarCraft Multi-Agent Challenge
IQL	19.42	84.87	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

0 of 13 row(s) selected.