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Smac On Smac 6H Vs 8Z 1

Metrics

Median Win Rate

Results

Performance results of various models on this benchmark

Model Name
Median Win Rate
Paper TitleRepository
IQL0The StarCraft Multi-Agent Challenge-
VDN0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
Heuristic0The StarCraft Multi-Agent Challenge-
DDN83.92DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
QMIX3Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning-
QMIX12.78DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
ACE93.75ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency-
VDN0The StarCraft Multi-Agent Challenge-
QPLEX-A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning-
QMIX3The StarCraft Multi-Agent Challenge-
DMIX49.43DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
DPLEX43.75A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning-
IQL0DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
DIQL0.00DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning-
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Smac On Smac 6H Vs 8Z 1 | SOTA | HyperAI