Multi-Agent Learning with Cooperation-Driven Optimization Dynamics
Multilayer Artificial Neural Networks trained via backpropagation are the basic blocks of many, more complex, classification algorithms. Their strength lies in the possibility of realizing, with arbitrary precision, any function. This result comes at the cost of the large number of involved parameters to be optimized. In this work, we propose a mechanism for cooperation, i.e., information exchange among several artificial neural networks, with the goal of reducing model complexity while maintaining performance. More precisely, we consider several "small" agents, i.e., containing fewer parameters than a reference "large" one, that during training share their predictions by incorporating th...
| Authors | Jarod Ketcha Kouakep, Sreyvi UANN, Timoteo Carletti |
|---|---|
| Primary category | cs.MA |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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