HyperAI
Startseite
Neuigkeiten
Neueste Forschungsarbeiten
Tutorials
Datensätze
Wiki
SOTA
LLM-Modelle
GPU-Rangliste
Veranstaltungen
Suche
Über
Deutsch
HyperAI
Toggle sidebar
Seite durchsuchen…
⌘
K
Startseite
SOTA
Smac 1
Smac On Smac Def Outnumbered Parallel
Smac On Smac Def Outnumbered Parallel
Metriken
Median Win Rate
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Median Win Rate
Paper Title
Repository
DIQL
0.0
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QMIX
30.0
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
IQL
0.0
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
DRIMA
70.0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
-
DMIX
5.0
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
COMA
0.0
Counterfactual Multi-Agent Policy Gradients
MASAC
0.0
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
VDN
0.0
Value-Decomposition Networks For Cooperative Multi-Agent Learning
DDN
0.0
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QTRAN
0.0
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
0 of 10 row(s) selected.
Previous
Next