The most data-efficient algorithms for reinforcement learning in robotics are
model-based policy search algorithms, which alternate between learning a
dynamical model of the robot and optimizing a policy to maximize the expected
return given the model and its uncertainties. Among the few proposed
approaches, the recently introduced Black-DROPS algorithm exploits a black-box
optimization algorithm to achieve both high data-efficiency and good
computation times when several cores are used; nevertheless, like all
model-based policy search approaches, Black-DROPS does not scale to high
dimensional state/action spaces. In this paper, we introduce a new model
learning procedure in Black-DROPS that leverages parameterized black-box priors
to (1) scale up to high-dimensional systems, and (2) be robust to large
inaccuracies of the prior information. We demonstrate the effectiveness of our
approach with the "pendubot" swing-up task in simulation and with a physical
hexapod robot (48D state space, 18D action space) that has to walk forward as
fast as possible. The results show that our new algorithm is more
data-efficient than previous model-based policy search algorithms (with and
without priors) and that it can allow a physical 6-legged robot to learn new
gaits in only 16 to 30 seconds of interaction time.Comment: Accepted at ICRA 2018; 8 pages, 4 figures, 2 algorithms, 1 table;
Video at https://youtu.be/HFkZkhGGzTo ; Spotlight ICRA presentation at
https://youtu.be/_MZYDhfWeL