  We show that it is possible to learn protocols that effect fast and efficient
state-to-state transformations in simulation models of active particles. By
encoding the protocol in the form of a neural network we use evolutionary
methods to identify protocols that take active particles from one steady state
to another, as quickly as possible or with as little energy expended as
possible. Our results show that protocols identified by a flexible
neural-network ansatz, which allows the optimization of multiple control
parameters and the emergence of sharp features, are more efficient than
protocols derived recently by constrained analytical methods. Our learning
scheme is straightforward to use in experiment, suggesting a way of designing
protocols for the efficient manipulation of active matter in the laboratory.
