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Benchmarks
Multi-Agent Reinforcement Learning on SMAC 3m v1
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100.9
Normalized Win Rate
MAST
8.548
32.524
56.5
80.476
Sep 28, 2024
Normalized Win Rate
Updated 4d ago
Evaluation Results
Method
Method
Links
Normalized Win Rate
MAST
Alg.=Q-MIX, Sparsity=9...
2024.09
100.9
MAST
Alg.=RES, Sparsity=95%...
2024.09
99.8
MAST
Alg.=WQ-MIX, Sparsity=...
2024.09
98.6
Tiny
Alg.=Q-MIX, Sparsity=9...
2024.09
98.3
Tiny
Alg.=WQ-MIX, Sparsity=...
2024.09
98.3
RLx2
Alg.=WQ-MIX, Sparsity=...
2024.09
98
RLx2
Alg.=RES, Sparsity=95%...
2024.09
97.9
SET
Alg.=WQ-MIX, Sparsity=...
2024.09
97.8
RigL
Alg.=WQ-MIX, Sparsity=...
2024.09
97.8
Tiny
Alg.=RES, Sparsity=95%...
2024.09
97.8
SET
Alg.=RES, Sparsity=95%...
2024.09
97.3
SS
Alg.=WQ-MIX, Sparsity=...
2024.09
96.9
SET
Alg.=Q-MIX, Sparsity=9...
2024.09
96
SS
Alg.=RES, Sparsity=95%...
2024.09
95.6
RigL
Alg.=Q-MIX, Sparsity=9...
2024.09
95.3
SS
Alg.=Q-MIX, Sparsity=9...
2024.09
91.6
RigL
Alg.=RES, Sparsity=95%...
2024.09
91.1
RLx2
Alg.=Q-MIX, Sparsity=9...
2024.09
12.1
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