  Performing Reinforcement Learning in sparse rewards settings, with very
little prior knowledge, is a challenging problem since there is no signal to
properly guide the learning process. In such situations, a good search strategy
is fundamental. At the same time, not having to adapt the algorithm to every
single problem is very desirable. Here we introduce TAXONS, a Task Agnostic
eXploration of Outcome spaces through Novelty and Surprise algorithm. Based on
a population-based divergent-search approach, it learns a set of diverse
policies directly from high-dimensional observations, without any task-specific
information. TAXONS builds a repertoire of policies while training an
autoencoder on the high-dimensional observation of the final state of the
system to build a low-dimensional outcome space. The learned outcome space,
combined with the reconstruction error, is used to drive the search for new
policies. Results show that TAXONS can find a diverse set of controllers,
covering a good part of the ground-truth outcome space, while having no
information about such space.
