HyperAI
HomeNewsLatest PapersTutorialsDatasetsWikiSOTALLM ModelsGPU LeaderboardEvents
Search
About
HyperAI

Smac On Smac Def Infantry Parallel

Metrics

Median Win Rate

Results

Performance results of various models on this benchmark

Model Name
Median Win Rate
Paper TitleRepository
DIQL45.0DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
DMIX90.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
IQL40.0The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
COMA50.0Counterfactual Multi-Agent Policy Gradients
DDN20.0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
QTRAN100.0QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
DRIMA100.0Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning-
QMIX95.0QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
MASAC30.0Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
VDN95.0Value-Decomposition Networks For Cooperative Multi-Agent Learning
0 of 10 row(s) selected.