  Neuroevolution is an alternative to gradient-based optimisation that has the
potential to avoid local minima and allows parallelisation. The main limiting
factor is that usually it does not scale well with parameter space
dimensionality. Inspired by recent work examining neural network intrinsic
dimension and loss landscapes, we hypothesise that there exists a
low-dimensional manifold, embedded in the policy network parameter space,
around which a high-density of diverse and useful policies are located. This
paper proposes a novel method for diversity-based policy search via
Neuroevolution, that leverages learned representations of the policy network
parameters, by performing policy search in this learned representation space.
Our method relies on the Quality-Diversity (QD) framework which provides a
principled approach to policy search, and maintains a collection of diverse
policies, used as a dataset for learning policy representations. Further, we
use the Jacobian of the inverse-mapping function to guide the search in the
representation space. This ensures that the generated samples remain in the
high-density regions, after mapping back to the original space. Finally, we
evaluate our contributions on four continuous-control tasks in simulated
environments, and compare to diversity-based baselines.
