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Benchmarks
Multi-Agent Reinforcement Learning on SMAC 6h* v1
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104.9
Normalized Win Rate
MAST
6.1
31.75
57.4
83.05
Sep 28, 2024
Normalized Win Rate
Updated 4d ago
Evaluation Results
Method
Method
Links
Normalized Win Rate
MAST
Alg.=RES, Sparsity=85%...
2024.09
104.9
MAST
Alg.=WQ-MIX, Sparsity=...
2024.09
98.4
MAST
Alg.=Q-MIX, Sparsity=9...
2024.09
97.6
Tiny
Alg.=RES, Sparsity=85%...
2024.09
83.3
RLx2
Alg.=RES, Sparsity=85%...
2024.09
72.7
SET
Alg.=Q-MIX, Sparsity=9...
2024.09
67.1
Tiny
Alg.=Q-MIX, Sparsity=9...
2024.09
58.2
RLx2
Alg.=WQ-MIX, Sparsity=...
2024.09
52.8
Tiny
Alg.=WQ-MIX, Sparsity=...
2024.09
51
RigL
Alg.=Q-MIX, Sparsity=9...
2024.09
48.7
SET
Alg.=WQ-MIX, Sparsity=...
2024.09
44.1
SET
Alg.=RES, Sparsity=85%...
2024.09
44.1
RigL
Alg.=WQ-MIX, Sparsity=...
2024.09
41
SS
Alg.=Q-MIX, Sparsity=9...
2024.09
40.2
SS
Alg.=RES, Sparsity=85%...
2024.09
39.1
RigL
Alg.=RES, Sparsity=85%...
2024.09
35.3
SS
Alg.=WQ-MIX, Sparsity=...
2024.09
29.6
RLx2
Alg.=Q-MIX, Sparsity=9...
2024.09
9.9
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