Home News Latest Papers Tutorials Datasets Wiki SOTA LLM Models GPU Leaderboard Events

English

Smac On Smac Corridor

Metrics

Average Score

Median Win Rate

Results

Performance results of various models on this benchmark

Model Name	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.