Multi-Agent Reinforcement Learning on SMAC MMM v1
99.7Natural ScoreIBAL
Evaluation Results
| Method | Links | |||||||
|---|---|---|---|---|---|---|---|---|
| IBAL2026.05 | 99.7 | 98.3 | 97 | 92.7 | 84.4 | 88.7 | 93.5 | |
| Vanilla QMIX2026.05 | 98.6 | 81.3 | 83.3 | 65.6 | 29.5 | 0 | 59 | |
| FGSM2026.05 | 98 | 92.3 | 98 | 73.8 | 57.9 | 7.3 | 71.2 | |
| ERNIE2026.05 | 98 | 94.7 | 94.3 | 69.8 | 41.2 | 2.1 | 66.3 | |
| Rand-Act2026.05 | 97.5 | 93.7 | 90.7 | 85.4 | 66.6 | 0 | 72.3 | |
| ATLA2026.05 | 97.5 | 94.3 | 95.7 | 85.4 | 72.3 | 3.1 | 74.7 | |
| Rand-Obs2026.05 | 96.5 | 93 | 93.7 | 80.2 | 59.9 | 0 | 70.6 | |
| WALL2026.05 | 95.9 | 93 | 96 | 92.4 | 95.6 | 13.5 | 81.1 | |
| ROMANCE2026.05 | 94.8 | 92.7 | 90.3 | 88.5 | 57.2 | 4.2 | 62.9 |