  Most policy search algorithms require thousands of training episodes to find
an effective policy, which is often infeasible with a physical robot. This
survey article focuses on the extreme other end of the spectrum: how can a
robot adapt with only a handful of trials (a dozen) and a few minutes? By
analogy with the word "big-data", we refer to this challenge as "micro-data
reinforcement learning". We show that a first strategy is to leverage prior
knowledge on the policy structure (e.g., dynamic movement primitives), on the
policy parameters (e.g., demonstrations), or on the dynamics (e.g.,
simulators). A second strategy is to create data-driven surrogate models of the
expected reward (e.g., Bayesian optimization) or the dynamical model (e.g.,
model-based policy search), so that the policy optimizer queries the model
instead of the real system. Overall, all successful micro-data algorithms
combine these two strategies by varying the kind of model and prior knowledge.
The current scientific challenges essentially revolve around scaling up to
complex robots (e.g., humanoids), designing generic priors, and optimizing the
computing time.
