检验Hebbian元学习中的基因组瓶颈假设
Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning
摘要：
Hebbian元学习最近显示出解决困难的强化学习问题的希望，允许代理在一定程度上适应环境的变化。然而，由于这些方法中的每个突触都可以学习非常特定的学习规则，因此推广到非常不同的情况的能力可能会降低。我们假设通过“基因组瓶颈”限制赫布学习规则的数量可以作为正则化器，从而更好地概括环境的变化。我们通过将Hebbian学习规则的数量与突触的数量解耦并系统地改变Hebbian学习规则的数量来测试这一假设。本文的结果表明，同时学习Hebbian学习规则及其分配给突触是一个困难的优化问题，导致在测试环境中性能不佳。然而，与我们平行的研究发现，通过将相似的规则聚类在一起，确实可以减少学习规则的数量。因此，如何最好地实现“基因组瓶颈”算法是一个重要的研究方向，值得进一步研究。
英文摘要：
Hebbian meta-learning has recently shown promise to solve hard reinforcement learning problems, allowing agents to adapt to some degree to changes in the environment. However, because each synapse in these approaches can learn a very specific learning rule, the ability to generalize to very different situations is likely reduced. We hypothesize that limiting the number of Hebbian learning rules through a "genomic bottleneck" can act as a regularizer leading to better generalization across changes to the environment. We test this hypothesis by decoupling the number of Hebbian learning rules from the number of synapses and systematically varying the number of Hebbian learning rules. The results in this paper suggest that simultaneously learning the Hebbian learning rules and their assignment to synapses is a difficult optimization problem, leading to poor performance in the environments tested. However, parallel research to ours finds that it is indeed possible to reduce the number of learning rules by clustering similar rules together. How to best implement a "genomic bottleneck" algorithm is thus an important research direction that warrants further investigation.
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