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Benchmarks
Multi-Agent Reinforcement Learning on SMAC corridor v2
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79.2
Win Rate
ACL-LFT
65.056
68.728
72.4
76.072
Oct 30, 2025
Win Rate
Updated 1mo ago
Evaluation Results
Method
Method
Links
Win Rate
ACL-LFT
RL-Method=QPLEX
2025.10
79.2
ACL-LFT
RL-Method=QMIX
2025.10
78.6
ACL-LFT
RL-Method=MAPPO
2025.10
77.9
AMAGO
RL-Method=QPLEX
2025.10
76.3
AMAGO
RL-Method=QMIX
2025.10
75
AMAGO
RL-Method=MAPPO
2025.10
74.3
Mamba
RL-Method=QPLEX
2025.10
73.5
ToST
RL-Method=QPLEX
2025.10
72.9
Mamba
RL-Method=QMIX
2025.10
71.2
Transformer
RL-Method=QPLEX
2025.10
70.6
ToST
RL-Method=QMIX
2025.10
70.3
Mamba
RL-Method=MAPPO
2025.10
69
Transformer
RL-Method=QMIX
2025.10
68.5
ToST
RL-Method=MAPPO
2025.10
68.1
Transformer
RL-Method=MAPPO
2025.10
65.6
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