Google ScholarSparse NNs for Control
Best Student Paper Award, ICANN 2025
Results
The following table compares our spatially embedded models to the best performing models (“expert”) from the Farama-Minari repository, trained with PPO, SAC, and TQC, by analyzing their architectures and weight counts. The “RWN” column indicates the ratio of non-zero weights in the spatial SNN compared to the SOTA model, while “RP” shows the relative performance.
| Environment | SOTA Weights | Spatial Weights | RWN (%) | SOTA Performance | Spatial Performance | RP (%) |
|---|---|---|---|---|---|---|
| Swimmer | 9,408 | 137 | 1.46 | 363.7 ± 1.8 | 361.8 ± 1.7 | 99.48 |
| Hopper | 347,392 | 222 | 0.06 | 4098.2 ± 247.7 | 2706.1 ± 73.7 | 66.03 |
| HalfCheetah | 384,256 | 703 | 0.18 | 17641.8 ± 61.9 | 3670.4 ± 1198.3 | 20.81 |
| Walker2d | 359,680 | 694 | 0.19 | 6956.6 ± 15.9 | 2492.0 ± 247.7 | 35.82 |
| Ant | 475,392 | 1,208 | 0.25 | 5846.3 ± 138.5 | 1346.8 ± 35.1 | 23.04 |