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ResearchFormerly WizWand

Multi-Agent Reinforcement Learning on SMAC Avg. v1

1.006Normalized Win RateMAST
0.269680.460840.6520.84316Sep 28, 2024
Updated 8mo ago

Evaluation results

MethodLinks
MASTAlg.=RES, Sparsity=89%, Total Size=0.122x, FLOPs (Train)=0.116x, FLOPs (Test)=0.112x2024.091.006
MASTAlg.=Q-MIX, Sparsity=92%, Total Size=0.086x, FLOPs (Train)=0.076x, FLOPs (Test)=0.075x2024.090.989
MASTAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.090.981
TinyAlg.=RES, Sparsity=89%, Total Size=0.122x, FLOPs (Train)=0.116x, FLOPs (Test)=0.112x2024.090.932
RLx2Alg.=RES, Sparsity=89%, Total Size=0.122x, FLOPs (Train)=0.116x, FLOPs (Test)=0.112x2024.090.877
SETAlg.=RES, Sparsity=89%, Total Size=0.122x, FLOPs (Train)=0.116x, FLOPs (Test)=0.112x2024.090.811
SSAlg.=RES, Sparsity=89%, Total Size=0.122x, FLOPs (Train)=0.116x, FLOPs (Test)=0.112x2024.090.791
RigLAlg.=RES, Sparsity=89%, Total Size=0.122x, FLOPs (Train)=0.116x, FLOPs (Test)=0.112x2024.090.785
TinyAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.090.774
TinyAlg.=Q-MIX, Sparsity=92%, Total Size=0.086x, FLOPs (Train)=0.076x, FLOPs (Test)=0.075x2024.090.771
RLx2Alg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.090.747
SETAlg.=Q-MIX, Sparsity=92%, Total Size=0.086x, FLOPs (Train)=0.076x, FLOPs (Test)=0.075x2024.090.732
SETAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.090.709
RigLAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.090.69
SSAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.090.661
RigLAlg.=Q-MIX, Sparsity=92%, Total Size=0.086x, FLOPs (Train)=0.076x, FLOPs (Test)=0.075x2024.090.646
SSAlg.=Q-MIX, Sparsity=92%, Total Size=0.086x, FLOPs (Train)=0.076x, FLOPs (Test)=0.075x2024.090.597
RLx2Alg.=Q-MIX, Sparsity=92%, Total Size=0.086x, FLOPs (Train)=0.076x, FLOPs (Test)=0.075x2024.090.298