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ResearchFormerly WizWand

Multi-Agent Reinforcement Learning on SMAC 2s3z v1

100.2Normalized Win RateMAST
43.62458.3127387.688Sep 28, 2024
Updated 8mo ago

Evaluation results

MethodLinks
MASTAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.09100.2
MASTAlg.=RES, Sparsity=90%, Total Size=0.111x, FLOPs (Train)=0.104x, FLOPs (Test)=0.100x2024.0998.4
MASTAlg.=Q-MIX, Sparsity=95%, Total Size=0.062x, FLOPs (Train)=0.051x, FLOPs (Test)=0.050x2024.0998
TinyAlg.=RES, Sparsity=90%, Total Size=0.111x, FLOPs (Train)=0.104x, FLOPs (Test)=0.100x2024.0996.5
RigLAlg.=RES, Sparsity=90%, Total Size=0.111x, FLOPs (Train)=0.104x, FLOPs (Test)=0.100x2024.0994.7
RLx2Alg.=RES, Sparsity=90%, Total Size=0.111x, FLOPs (Train)=0.104x, FLOPs (Test)=0.100x2024.0994
SSAlg.=RES, Sparsity=90%, Total Size=0.111x, FLOPs (Train)=0.104x, FLOPs (Test)=0.100x2024.0992.8
SETAlg.=RES, Sparsity=90%, Total Size=0.111x, FLOPs (Train)=0.104x, FLOPs (Test)=0.100x2024.0992.8
TinyAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.0989.6
RLx2Alg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.0987.3
RigLAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.0986.8
SETAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.0985.9
TinyAlg.=Q-MIX, Sparsity=95%, Total Size=0.062x, FLOPs (Train)=0.051x, FLOPs (Test)=0.050x2024.0983.7
SETAlg.=Q-MIX, Sparsity=95%, Total Size=0.062x, FLOPs (Train)=0.051x, FLOPs (Test)=0.050x2024.0977.6
SSAlg.=WQ-MIX, Sparsity=90%, Total Size=0.106x, FLOPs (Train)=0.100x, FLOPs (Test)=0.100x2024.0975.4
SSAlg.=Q-MIX, Sparsity=95%, Total Size=0.062x, FLOPs (Train)=0.051x, FLOPs (Test)=0.050x2024.0973
RigLAlg.=Q-MIX, Sparsity=95%, Total Size=0.062x, FLOPs (Train)=0.051x, FLOPs (Test)=0.050x2024.0969.4
RLx2Alg.=Q-MIX, Sparsity=95%, Total Size=0.062x, FLOPs (Train)=0.051x, FLOPs (Test)=0.050x2024.0945.8