الرئيسية الأخبار أحدث الأوراق البحثية الدروس مجموعات البيانات الموسوعة SOTA نماذج LLM لوحة الأداء GPU الفعاليات

العربية

Smac On Smac Def Infantry Parallel

المقاييس

Median Win Rate

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	Median Win Rate	Paper Title	Repository
DIQL	45.0	DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
DMIX	90.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL	40.0	The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
COMA	50.0	Counterfactual Multi-Agent Policy Gradients
DDN	20.0	DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QTRAN	100.0	QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
DRIMA	100.0	Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning	-
QMIX	95.0	QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
MASAC	30.0	Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
VDN	95.0	Value-Decomposition Networks For Cooperative Multi-Agent Learning

0 of 10 row(s) selected.

Smac On Smac Def Infantry Parallel | SOTA | HyperAI