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Benchmarks
Multi-Agent Reinforcement Learning on SMAC 2s3z v1
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100.2
Normalized Win Rate
MAST
43.624
58.312
73
87.688
Sep 28, 2024
Normalized Win Rate
Updated 4d ago
Evaluation Results
Method
Method
Links
Normalized Win Rate
MAST
Alg.=WQ-MIX, Sparsity=...
2024.09
100.2
MAST
Alg.=RES, Sparsity=90%...
2024.09
98.4
MAST
Alg.=Q-MIX, Sparsity=9...
2024.09
98
Tiny
Alg.=RES, Sparsity=90%...
2024.09
96.5
RigL
Alg.=RES, Sparsity=90%...
2024.09
94.7
RLx2
Alg.=RES, Sparsity=90%...
2024.09
94
SS
Alg.=RES, Sparsity=90%...
2024.09
92.8
SET
Alg.=RES, Sparsity=90%...
2024.09
92.8
Tiny
Alg.=WQ-MIX, Sparsity=...
2024.09
89.6
RLx2
Alg.=WQ-MIX, Sparsity=...
2024.09
87.3
RigL
Alg.=WQ-MIX, Sparsity=...
2024.09
86.8
SET
Alg.=WQ-MIX, Sparsity=...
2024.09
85.9
Tiny
Alg.=Q-MIX, Sparsity=9...
2024.09
83.7
SET
Alg.=Q-MIX, Sparsity=9...
2024.09
77.6
SS
Alg.=WQ-MIX, Sparsity=...
2024.09
75.4
SS
Alg.=Q-MIX, Sparsity=9...
2024.09
73
RigL
Alg.=Q-MIX, Sparsity=9...
2024.09
69.4
RLx2
Alg.=Q-MIX, Sparsity=9...
2024.09
45.8
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