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

Reuse of Neural Modules for General Video Game Playing