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Benchmarks
Multi-Agent Reinforcement Learning on SMAC 3s5z v1
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99.4
Normalized Win Rate
MAST
31.384
49.042
66.7
84.358
Sep 28, 2024
Normalized Win Rate
Updated 4d ago
Evaluation Results
Method
Method
Links
Normalized Win Rate
MAST
Alg.=RES, Sparsity=85%...
2024.09
99.4
MAST
Alg.=Q-MIX, Sparsity=9...
2024.09
99
MAST
Alg.=WQ-MIX, Sparsity=...
2024.09
96.1
Tiny
Alg.=RES, Sparsity=85%...
2024.09
95.1
RigL
Alg.=RES, Sparsity=85%...
2024.09
92.8
SET
Alg.=RES, Sparsity=85%...
2024.09
90.3
SS
Alg.=RES, Sparsity=85%...
2024.09
89
RLx2
Alg.=RES, Sparsity=85%...
2024.09
86.2
Tiny
Alg.=WQ-MIX, Sparsity=...
2024.09
70.7
Tiny
Alg.=Q-MIX, Sparsity=9...
2024.09
68.2
SS
Alg.=WQ-MIX, Sparsity=...
2024.09
62.5
RLx2
Alg.=WQ-MIX, Sparsity=...
2024.09
60.7
SET
Alg.=WQ-MIX, Sparsity=...
2024.09
56
SET
Alg.=Q-MIX, Sparsity=9...
2024.09
52.3
RigL
Alg.=WQ-MIX, Sparsity=...
2024.09
50.4
RLx2
Alg.=Q-MIX, Sparsity=9...
2024.09
50.1
RigL
Alg.=Q-MIX, Sparsity=9...
2024.09
45.2
SS
Alg.=Q-MIX, Sparsity=9...
2024.09
34
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