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Models/DIQL

DIQL

Reported on 42 benchmarks across 2 tasks · 2 papers

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology21 results

Multi-agent Reinforcement LearningonSMAC 3s5z_vs_3s6z
Average Score· 2021-02-16
17.52
best: 20.94 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonSMAC 3s5z_vs_3s6z
Median Win Rate· 2021-02-16
62.22
best: 100 (ACE)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonSMAC corridor
Average Score· 2021-02-16
19.68
best: 20 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonSMAC corridor
Median Win Rate· 2021-02-16
91.62
best: 100 (ACE)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonSMAC MMM2
Average Score· 2021-02-16
19.21
best: 20.9 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonSMAC MMM2
Median Win Rate· 2021-02-16
85.23
best: 100 (ACE)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonSMAC 6h_vs_8z
Average Score· 2021-02-16
14.94
best: 19.4 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonSMAC 27m_vs_30m
Average Score· 2021-02-16
14.45
best: 19.71 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonSMAC 27m_vs_30m
Median Win Rate· 2021-02-16
6.02
best: 91.48 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonDef_Armored_sequential
Median Win Rate· 2021-02-16
53.1
best: 100 (DRIMA)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonDef_Infantry_sequential
Median Win Rate· 2021-02-16
93.8
best: 100 (MADDPG)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
Multi-agent Reinforcement LearningonDef_Infantry_parallel
Median Win Rate· 2019-04-03
45
best: 100 (QTRAN)
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation arXiv:1904.02216
Multi-agent Reinforcement LearningonSMAC 6h_vs_8z
Median Win Rate
0
best: 93.75 (ACE)
Multi-agent Reinforcement LearningonOff_Hard_parallel
Median Win Rate
0
best: 80 (DRIMA)
Multi-agent Reinforcement LearningonDef_Outnumbered_sequential
Median Win Rate
0
best: 100 (DRIMA)
Multi-agent Reinforcement LearningonOff_Complicated_parallel
Median Win Rate
0
best: 100 (DRIMA)
Multi-agent Reinforcement LearningonOff_Near_parallel
Median Win Rate
0
best: 95 (QMIX)
Multi-agent Reinforcement LearningonDef_Armored_parallel
Median Win Rate
0
best: 90 (DMIX)
Multi-agent Reinforcement LearningonOff_Distant_parallel
Median Win Rate
0
best: 95 (DRIMA)
Multi-agent Reinforcement LearningonDef_Outnumbered_parallel
Median Win Rate
0
best: 70 (DRIMA)
Multi-agent Reinforcement LearningonOff_Superhard_parallel
Median Win Rate
0

Playing Games21 results

SMAConSMAC 3s5z_vs_3s6z
Average Score· 2021-02-16
17.52
best: 20.94 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConSMAC 3s5z_vs_3s6z
Median Win Rate· 2021-02-16
62.22
best: 100 (ACE)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConSMAC corridor
Average Score· 2021-02-16
19.68
best: 20 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConSMAC corridor
Median Win Rate· 2021-02-16
91.62
best: 100 (ACE)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConSMAC MMM2
Average Score· 2021-02-16
19.21
best: 20.9 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConSMAC MMM2
Median Win Rate· 2021-02-16
85.23
best: 100 (ACE)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConSMAC 6h_vs_8z
Average Score· 2021-02-16
14.94
best: 19.4 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConSMAC 27m_vs_30m
Average Score· 2021-02-16
14.45
best: 19.71 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConSMAC 27m_vs_30m
Median Win Rate· 2021-02-16
6.02
best: 91.48 (DDN)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConDef_Armored_sequential
Median Win Rate· 2021-02-16
53.1
best: 100 (DRIMA)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConDef_Infantry_sequential
Median Win Rate· 2021-02-16
93.8
best: 100 (MADDPG)
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning arXiv:2102.07936
SMAConDef_Infantry_parallel
Median Win Rate· 2019-04-03
45
best: 100 (QTRAN)
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation arXiv:1904.02216
SMAConSMAC 6h_vs_8z
Median Win Rate
0
best: 93.75 (ACE)
SMAConOff_Hard_parallel
Median Win Rate
0
best: 80 (DRIMA)
SMAConDef_Outnumbered_sequential
Median Win Rate
0
best: 100 (DRIMA)
SMAConOff_Complicated_parallel
Median Win Rate
0
best: 100 (DRIMA)
SMAConOff_Near_parallel
Median Win Rate
0
best: 95 (QMIX)
SMAConDef_Armored_parallel
Median Win Rate
0
best: 90 (DMIX)
SMAConOff_Distant_parallel
Median Win Rate
0
best: 95 (DRIMA)
SMAConDef_Outnumbered_parallel
Median Win Rate
0
best: 70 (DRIMA)
SMAConOff_Superhard_parallel
Median Win Rate
0