Hebbian学习规则的演变与融合：通过减少规则数量来提高泛化能力
Evolving and Merging Hebbian Learning Rules: Increasing Generalization by Decreasing the Number of Rules
摘要：
在训练后推广到分布外（OOD）的情况仍然是人工代理的挑战。为了提高鲁棒性所显示的塑料赫布神经网络，我们发展了一套赫布学习规则，其中多个连接分配给一个单一的规则。受基因组瓶颈的生物学现象的启发，我们证明了通过允许网络中的多个连接共享相同的局部学习规则，可以大幅减少可训练参数的数量，同时获得更强大的代理。在进化过程中，通过迭代地使用简单的K-Means聚类来组合规则，我们的Evolve and Merge方法能够将可训练参数的数量从61，440减少到1，920，同时提高鲁棒性，所有这些都不会增加所使用的代数。虽然优化的代理是在一个标准的四足机器人形态，我们评估代理的性能轻微的形态修改，共30看不见的形态。我们的研究结果补充了关于泛化、过拟合和面向对象自适应的讨论。为了创建能够适应更广泛的意外情况的代理，Hebbian学习结合规则化的“基因组瓶颈”可能是一个有前途的研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
