  Curriculum learning allows complex tasks to be mastered via incremental
progression over `stepping stone' goals towards a final desired behaviour.
Typical implementations learn locomotion policies for challenging environments
through gradual complexification of a terrain mesh generated through a
parameterised noise function. To date, researchers have predominantly generated
terrains from a limited range of noise functions, and the effect of the
generator on the learning process is underrepresented in the literature. We
compare popular noise-based terrain generators to two indirect encodings, CPPN
and GAN. To allow direct comparison between both direct and indirect
representations, we assess the impact of a range of representation-agnostic
MAP-Elites feature descriptors that compute metrics directly from the generated
terrain meshes. Next, performance and coverage are assessed when training a
humanoid robot in a physics simulator using the PPO algorithm. Results describe
key differences between the generators that inform their use in curriculum
learning, and present a range of useful feature descriptors for uptake by the
community.
