Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reducing catastrophic forgetting when evolving neural networks   A key stepping stone in the development of an artificial general intelligence
(a machine that can perform any task), is the production of agents that can
perform multiple tasks at once instead of just one. Unfortunately, canonical
methods are very prone to catastrophic forgetting (CF) - the act of overwriting
previous knowledge about a task when learning a new task. Recent efforts have
developed techniques for overcoming CF in learning systems, but no attempt has
been made to apply these new techniques to evolutionary systems. This research
presents a novel technique, weight protection, for reducing CF in evolutionary
systems by adapting a method from learning systems. It is used in conjunction
with other evolutionary approaches for overcoming CF and is shown to be
effective at alleviating CF when applied to a suite of reinforcement learning
tasks. It is speculated that this work could indicate the potential for a wider
application of existing learning-based approaches to evolutionary systems and
that evolutionary techniques may be competitive with or better than learning
systems when it comes to reducing CF.

8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat