  A general approach to knowledge transfer is introduced in which an agent
controlled by a neural network adapts how it reuses existing networks as it
learns in a new domain. Networks trained for a new domain can improve their
performance by routing activation selectively through previously learned neural
structure, regardless of how or for what it was learned. A neuroevolution
implementation of this approach is presented with application to
high-dimensional sequential decision-making domains. This approach is more
general than previous approaches to neural transfer for reinforcement learning.
It is domain-agnostic and requires no prior assumptions about the nature of
task relatedness or mappings. The method is analyzed in a stochastic version of
the Arcade Learning Environment, demonstrating that it improves performance in
some of the more complex Atari 2600 games, and that the success of transfer can
be predicted based on a high-level characterization of game dynamics.
