  Generalization to out-of-distribution (OOD) circumstances after training
remains a challenge for artificial agents. To improve the robustness displayed
by plastic Hebbian neural networks, we evolve a set of Hebbian learning rules,
where multiple connections are assigned to a single rule. Inspired by the
biological phenomenon of the genomic bottleneck, we show that by allowing
multiple connections in the network to share the same local learning rule, it
is possible to drastically reduce the number of trainable parameters, while
obtaining a more robust agent. During evolution, by iteratively using simple
K-Means clustering to combine rules, our Evolve and Merge approach is able to
reduce the number of trainable parameters from 61,440 to 1,920, while at the
same time improving robustness, all without increasing the number of
generations used. While optimization of the agents is done on a standard
quadruped robot morphology, we evaluate the agents' performances on slight
morphology modifications in a total of 30 unseen morphologies. Our results add
to the discussion on generalization, overfitting and OOD adaptation. To create
agents that can adapt to a wider array of unexpected situations, Hebbian
learning combined with a regularising "genomic bottleneck" could be a promising
research direction.
