Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning   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.

37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess