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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 TitleRepository
DIQL19.6891.62DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
DPLEX19.0881.25A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning-
VDN19.4785.34DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
IQL-0The StarCraft Multi-Agent Challenge-
DDN2095.4DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
Heuristic-0The StarCraft Multi-Agent Challenge-
QMIX15.0737.61DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
DMIX19.6690.45DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
QPLEX18.7375.00A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning-
ACE-100ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency-
QMIX-1Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning-
QMIX-1The StarCraft Multi-Agent Challenge-
IQL19.4284.87DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
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Smac On Smac Corridor | SOTA | HyperAI