Multi-Agent Reinforcement Learning on SMAC 8m v1
99.3Natural PerformanceIBAL
Evaluation Results
| Method | Links | |||||||
|---|---|---|---|---|---|---|---|---|
| IBAL2026.05 | 99.3 | 94.5 | 95.6 | 97.6 | 79 | 88.4 | 92.4 | |
| ROMANCE2026.05 | 98.1 | 89.8 | 90.9 | 90 | 33.3 | 11 | 68.8 | |
| Rand-Obs2026.05 | 98 | 85.6 | 88.8 | 74 | 29.2 | 6.2 | 63.6 | |
| FGSM2026.05 | 98 | 90.7 | 96.5 | 86.4 | 41.7 | 10.4 | 70.3 | |
| Rand-Act2026.05 | 97.6 | 84 | 79.1 | 85.2 | 28.1 | 6.2 | 63.4 | |
| ERNIE2026.05 | 96.9 | 84.4 | 90.7 | 83.4 | 26.7 | 6.2 | 64.2 | |
| WALL2026.05 | 96.5 | 92.2 | 92.7 | 93.7 | 85.5 | 13.5 | 79 | |
| Vanilla QMIX2026.05 | 92.8 | 83 | 75.7 | 70.8 | 14.6 | 7.3 | 57.3 | |
| ATLA2026.05 | 92.7 | 80 | 90.3 | 74 | 39.6 | 15.6 | 65.4 |